diff --git a/.clang-format b/.clang-format new file mode 100644 index 0000000..d1e8a7e --- /dev/null +++ b/.clang-format @@ -0,0 +1,137 @@ +--- +Language: Cpp +# BasedOnStyle: LLVM +AccessModifierOffset: -2 +AlignAfterOpenBracket: Align +AlignConsecutiveMacros: false +AlignConsecutiveAssignments: false +AlignConsecutiveDeclarations: false +AlignEscapedNewlines: Right +AlignOperands: true +AlignTrailingComments: true +AllowAllArgumentsOnNextLine: true +AllowAllConstructorInitializersOnNextLine: true +AllowAllParametersOfDeclarationOnNextLine: true +AllowShortBlocksOnASingleLine: Never +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: All +AllowShortLambdasOnASingleLine: All +AllowShortIfStatementsOnASingleLine: Never +AllowShortLoopsOnASingleLine: false +AlwaysBreakAfterDefinitionReturnType: None +AlwaysBreakAfterReturnType: None +AlwaysBreakBeforeMultilineStrings: false +AlwaysBreakTemplateDeclarations: MultiLine +BinPackArguments: true +BinPackParameters: false +BraceWrapping: + AfterCaseLabel: false + AfterClass: false + AfterControlStatement: false + AfterEnum: false + AfterFunction: false + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: false + AfterUnion: false + AfterExternBlock: false + BeforeCatch: false + BeforeElse: false + IndentBraces: false + SplitEmptyFunction: true + SplitEmptyRecord: true + SplitEmptyNamespace: true +BreakBeforeBinaryOperators: None +BreakBeforeBraces: Attach +BreakBeforeInheritanceComma: false +BreakInheritanceList: BeforeColon +BreakBeforeTernaryOperators: true +BreakConstructorInitializersBeforeComma: false +BreakConstructorInitializers: BeforeColon +BreakAfterJavaFieldAnnotations: false +BreakStringLiterals: true +ColumnLimit: 120 +CommentPragmas: '^ IWYU pragma:' +CompactNamespaces: false +ConstructorInitializerAllOnOneLineOrOnePerLine: false +ConstructorInitializerIndentWidth: 4 +ContinuationIndentWidth: 4 +Cpp11BracedListStyle: true +DeriveLineEnding: true +DerivePointerAlignment: false +DisableFormat: false +ExperimentalAutoDetectBinPacking: false +FixNamespaceComments: true +ForEachMacros: + - foreach + - Q_FOREACH + - BOOST_FOREACH +IncludeBlocks: Preserve +IncludeCategories: + - Regex: '^"(llvm|llvm-c|clang|clang-c)/' + Priority: 2 + SortPriority: 0 + - Regex: '^(<|"(gtest|gmock|isl|json)/)' + Priority: 3 + SortPriority: 0 + - Regex: '.*' + Priority: 1 + SortPriority: 0 +IncludeIsMainRegex: '(Test)?$' +IncludeIsMainSourceRegex: '' +IndentCaseLabels: false +IndentGotoLabels: true +IndentPPDirectives: None +IndentWidth: 2 +IndentWrappedFunctionNames: false +JavaScriptQuotes: Leave +JavaScriptWrapImports: true +KeepEmptyLinesAtTheStartOfBlocks: true +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCBinPackProtocolList: Auto +ObjCBlockIndentWidth: 2 +ObjCSpaceAfterProperty: false +ObjCSpaceBeforeProtocolList: true +PenaltyBreakAssignment: 2 +PenaltyBreakBeforeFirstCallParameter: 19 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +PenaltyBreakTemplateDeclaration: 10 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 60 +PointerAlignment: Right +ReflowComments: true +SortIncludes: true +SortUsingDeclarations: true +SpaceAfterCStyleCast: false +SpaceAfterLogicalNot: false +SpaceAfterTemplateKeyword: true +SpaceBeforeAssignmentOperators: true +SpaceBeforeCpp11BracedList: false +SpaceBeforeCtorInitializerColon: true +SpaceBeforeInheritanceColon: true +SpaceBeforeParens: ControlStatements +SpaceBeforeRangeBasedForLoopColon: true +SpaceInEmptyBlock: false +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 1 +SpacesInAngles: false +SpacesInConditionalStatement: false +SpacesInContainerLiterals: true +SpacesInCStyleCastParentheses: false +SpacesInParentheses: false +SpacesInSquareBrackets: false +SpaceBeforeSquareBrackets: false +Standard: Latest +StatementMacros: + - Q_UNUSED + - QT_REQUIRE_VERSION +TabWidth: 8 +UseCRLF: false +UseTab: Never +... + diff --git a/.gitignore b/.gitignore index 4da0e09..267f985 100644 --- a/.gitignore +++ b/.gitignore @@ -1,13 +1,15 @@ .idea -cmake-build-debug -cmake-build-release -cmake-build-matlab +cmake-build-debug* +cmake-build-release* +cmake-build-matlab* venv */*/__pycache__ +*/*/*/__pycache__ .ipynb_checkpoints/ */.ipynb_checkpoints/ +data/figures data/MKL/*.csv data/MKL/*/*.csv diff --git a/.travis.yml b/.travis.yml index 7c991ee..af18535 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,43 +1,58 @@ language: cpp -dist: bionic -compiler: - - gcc + +branches: + only: + - master + +dist: focal + +jobs: + include: + - os: linux + env: CC=gcc-8 CXX=g++-8 TOOLSET=gcc COMPILER=g++-8 + - os: linux + env: CC=gcc-10 CXX=g++-10 TOOLSET=gcc COMPILER=g++-10 + - os: linux + env: CC=clang-10 CXX=clang++-10 TOOLSET=clang COMPILER=clang++-10 + addons: apt: -# sources: -# - ubuntu-toolchain-r-test + sources: + - sourceline: 'ppa:ubuntu-toolchain-r/test' packages: - gcc-8 - g++-8 - - cmake + - gcc-10 + - g++-10 + - clang-10 + - clang++-10 + - libomp-dev - libgtest-dev - -before_install: - - wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB - - sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB - - sudo wget https://apt.repos.intel.com/setup/intelproducts.list -O /etc/apt/sources.list.d/intelproducts.list - - sudo apt-get update - - sudo apt-get install intel-mkl-2019.5-075 --allow-unauthenticated + - libopenblas-dev install: - - if [ "$C" = "gcc" ]; then export C="gcc-8"; fi - - if [ "$CXX" = "g++" ]; then export CXX="g++-8"; fi + # Extract CMake 3.17.5 + - wget https://github.com/Kitware/CMake/releases/download/v3.17.5/cmake-3.17.5-Linux-x86_64.tar.gz -qO- | tar xvzf - -C . + - export CMAKE_PATH=`pwd`/cmake-3.17.5-Linux-x86_64/bin + # Install GTest - pushd . - cd /usr/src/gtest - sudo mkdir build - cd build - - sudo cmake .. + - sudo ${CMAKE_PATH}/cmake .. - sudo make - - sudo make install - popd -script: - - source /opt/intel/bin/compilervars.sh intel64 -platform linux +before_script: + - export PATH=`pwd`/cmake-3.17.5-Linux-x86_64/bin:$PATH - mkdir build - cd build - - cmake -DCMAKE_BUILD_TYPE=RELEASE -DWITH_TRAVIS=On -DWITH_TESTS=On -DWITH_MKL=On -DWITH_BLIS=Off -DWITH_OPENBLAS=Off .. + # TODO: Add if statement to check an environment variable specifying either MKL or OpenBLAS and use appropriate lib + - cmake -DCMAKE_BUILD_TYPE=RELEASE -DWITH_TRAVIS=On -DWITH_TESTS=On -DWITH_OPENBLAS=On .. - VERBOSE=1 cmake --build . + +script: - ctest -VV notifications: diff --git a/CMakeLists.txt b/CMakeLists.txt index 07d80cb..dc5dbed 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,300 +1,273 @@ -#@HEADER -# ****************************************************************************** -# -# CP-CALS: Software for computing the Canonical Polyadic Decomposition using -# the Concurrent Alternating Least Squares Algorithm. -# -# Copyright (c) 2020, Christos Psarras -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -# ****************************************************************************** -#@HEADER - -CMAKE_MINIMUM_REQUIRED(VERSION 3.12.4 FATAL_ERROR) -PROJECT(CALS) +cmake_minimum_required(VERSION 3.17.5 FATAL_ERROR) +project(CP-CALS LANGUAGES CXX) include(CheckCXXCompilerFlag) - -OPTION(WITH_MKL "Create MKL Library version" Off) -OPTION(WITH_CUBLAS "Create cuBLAS Library version" Off) -OPTION(WITH_BLIS "Create BLIS Library version" Off) -OPTION(WITH_OPENBLAS "Create OpenBLAS Library version" Off) -OPTION(WITH_MATLAB "Enable MATLAB support" Off) -OPTION(WITH_TESTS "Enable Tests" Off) -OPTION(WITH_TRAVIS "Travis mode" Off) # There is a bug with cmake on travis, disable specifying BLAS version for now +include(cmake/utils.cmake) + +option(WITH_MKL "Create MKL Library version" Off) +option(WITH_BLIS "Create BLIS Library version" Off) +option(WITH_OPENBLAS "Create OpenBLAS Library version" Off) +option(WITH_CUBLAS "Create cuBLAS Library version" Off) +option(WITH_MATLAB "Enable MATLAB support" Off) +option(WITH_TESTS "Enable Tests" Off) +option(WITH_TRAVIS "Travis mode" Off) # There is a bug with cmake on travis, disable specifying BLAS version for now +option(WITH_DIAGNOSTICS "Set level of diagnostics during compilation (0, 1, 2) " 2) +option(WITH_EXPERIMENTS "Enable experiments that make use of external software for comparisons (CTF, PLANC)" Off) + +if (WITH_DIAGNOSTICS GREATER_EQUAL 1) + message(STATUS "Enabling verbose Makefile") + set(CMAKE_VERBOSE_MAKEFILE ON) +endif () ######################################################################################### -# Find MKL +# Find OpenMP ######################################################################################### -if (${WITH_MKL}) - if (NOT ${WITH_TRAVIS}) - set(BLA_VENDOR Intel10_64lp) - endif () - FIND_PACKAGE(BLAS REQUIRED) - FIND_PACKAGE(LAPACK REQUIRED) - - FIND_PATH(CALS_INCLUDE_DIRS mkl.h $ENV{MKLROOT}/include) - SET(CALS_LINK_LIBRARIES ${BLAS_LIBRARIES} ${LAPACK_LIBRARIES} m) - - SET(BLA_VEN MKL) -endif () +FIND_PACKAGE(OpenMP REQUIRED COMPONENTS CXX) +message(STATUS "${OpenMP_CXX_LIBRARIES}") ######################################################################################### -# Find OpenMP +# Find CUBLAS ######################################################################################### -FIND_PACKAGE(OpenMP REQUIRED) -if (OPENMP_FOUND) - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") - set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}") +if (WITH_CUBLAS) + ENABLE_LANGUAGE(CUDA) + FIND_PACKAGE(CUDAToolkit REQUIRED) endif () ######################################################################################### -# Find CUDA +# Find MKL ######################################################################################### -if (${WITH_CUBLAS}) - ENABLE_LANGUAGE(CUDA) - SET(CALS_INCLUDE_DIRS ${CALS_INCLUDE_DIRS} ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}) - SET(CALS_LINK_LIBRARIES ${CALS_LINK_LIBRARIES} -lcublas -lcudart) - IF (EXISTS "/usr/local/cuda/lib64") - SET (CALS_LINK_DIRECTORIES "/usr/local/cuda/lib64") - ELSE () - SET (CALS_LINK_DIRECTORIES "$ENV{CUDA_ROOT}/lib64" "$ENV{CUDA_ROOT}/lib64/lib64") - ENDIF () - MESSAGE (STATUS CALS_LINK_DIRECTORIES: ${CALS_LINK_DIRECTORIES}) +if (WITH_MKL) + if (NOT WITH_TRAVIS) + set(BLA_VENDOR Intel10_64lp) + endif () + find_package(BLAS REQUIRED) + find_package(LAPACK REQUIRED) + + # If compiler is GNU, make sure that CALS links to the GOMP version of MKL + if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU") + + # TODO: Supposed to be a better way of doing this using list(TRANSFORM ...), but I couldn't figure it out + set(_BLAS_LIBRARIES) + foreach (INC ${BLAS_LIBRARIES}) + string(REGEX REPLACE ".*/libiomp5.so" "-lgomp" INC ${INC}) + string(REPLACE "intel_thread" "gnu_thread" INC ${INC}) + LIST(APPEND _BLAS_LIBRARIES ${INC}) + endforeach () + set(BLAS_LIBRARIES ${_BLAS_LIBRARIES}) + set(_LAPACK_LIBRARIES) + foreach (INC ${LAPACK_LIBRARIES}) + string(REGEX REPLACE ".*/libiomp5.so" "-lgomp" INC ${INC}) + string(REPLACE "intel_thread" "gnu_thread" INC ${INC}) + LIST(APPEND _LAPACK_LIBRARIES ${INC}) + endforeach () + set(LAPACK_LIBRARIES ${_LAPACK_LIBRARIES}) + message(STATUS "${BLAS_LIBRARIES}") + message(STATUS "${LAPACK_LIBRARIES}") + endif () + + set(BLA_VEN MKL) endif () ######################################################################################### # Find BLIS ######################################################################################### -if (${WITH_BLIS}) - if(NOT ${WITH_MKL}) - MESSAGE(FATAL_ERROR "!! BLIS does not provide LAPACK functions. Please use -DWITH_MKL=On to provide LAPACK from MKL.") +if (WITH_BLIS) + if (NOT WITH_TRAVIS) + set(BLA_VENDOR FLAME) endif () - MESSAGE("BLIS Install dir: " "$ENV{BLIS_INSTALL_DIR}") - SET(BLIS_LIBRARIES $ENV{BLIS_INSTALL_DIR}/lib/libblis.so) - - SET(CALS_INCLUDE_DIRS $ENV{BLIS_INSTALL_DIR}/include/blis ${CALS_INCLUDE_DIRS}) - SET(CALS_LINK_LIBRARIES ${BLIS_LIBRARIES} ${LAPACK_LIBRARIES} m) + find_package(BLAS REQUIRED) + find_package(LAPACK REQUIRED) - SET(BLA_VEN BLIS) + set(BLA_VEN FLAME) endif () ######################################################################################### # Find OpenBLAS ######################################################################################### -if (${WITH_OPENBLAS}) - if(NOT ${WITH_MKL}) - MESSAGE(FATAL_ERROR "!! Please use -DWITH_MKL=On to provide LAPACK from MKL (for OpenBLAS due to Bug).") +if (WITH_OPENBLAS) + if (NOT WITH_TRAVIS) + set(BLA_VENDOR OpenBLAS) endif () - MESSAGE("OPENBLAS Install dir: " "$ENV{OPENBLAS_INSTALL_DIR}") - SET(OPENBLAS_LIBRARIES $ENV{OPENBLAS_INSTALL_DIR}/lib/libopenblas.so) + find_package(BLAS REQUIRED) + find_package(LAPACK REQUIRED) - SET(CALS_INCLUDE_DIRS $ENV{OPENBLAS_INSTALL_DIR}/include ${CALS_INCLUDE_DIRS}) - SET(CALS_LINK_LIBRARIES ${OPENBLAS_LIBRARIES} ${LAPACK_LIBRARIES} m) - - SET(BLA_VEN OPENBLAS) + set(BLA_VEN OPENBLAS) endif () -################################ -# Find Matlab -################################ -IF (WITH_MATLAB) - OPTION(MATLAB_PATH "Path to MATLAB installation") - IF (MATLAB_PATH) +######################################################################################### +# Find MATLAB +######################################################################################### +if (WITH_MATLAB) + option(MATLAB_PATH "Path to MATLAB installation") + if (MATLAB_PATH) SET(Matlab_ROOT_DIR "${MATLAB_PATH}") - ELSE () + else () SET(Matlab_ROOT_DIR "$ENV{HOME}/opt/Matlab2018b") - ENDIF () + endif () + + message("MATLAB_ROOT_DIR: " ${MATLAB_PATH}) + find_package(Matlab COMPONENTS MX_LIBRARY REQUIRED) - MESSAGE("MATLAB_ROOT_DIR: " ${MATLAB_PATH}) - FIND_PACKAGE(Matlab COMPONENTS MX_LIBRARY REQUIRED) + set(CALS_LINK_LIBRARIES ${CALS_LINK_LIBRARIES} ${Matlab_MEX_LIBRARY} ${Matlab_MX_LIBRARY}) - SET(CALS_LINK_LIBRARIES ${CALS_LINK_LIBRARIES} ${Matlab_MEX_LIBRARY} ${Matlab_MX_LIBRARY}) + set(HAVE_MATLAB ON) - SET(HAVE_MATLAB ON) - SET(BLA_VEN Matlab) -ENDIF () + set(BLA_VEN MATLAB) +endif () ######################################################################################### -# General Configuration +# Configuration ######################################################################################### -SET(CMAKE_VERBOSE_MAKEFILE 1) -SET(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD 17) + +# CP_CALS Library +set(CP_CALS CALS_${BLA_VEN}) +add_library(${CP_CALS} SHARED) + +target_sources(${CP_CALS} + PRIVATE + src/als.cpp + src/cals.cpp + src/tensor.cpp + src/matrix.cpp + src/ktensor.cpp + src/cals_blas.cpp + src/cuda_utils.cpp + src/utils/utils.cpp + src/utils/error.cpp + src/utils/mttkrp.cpp + src/utils/update.cpp + src/multi_ktensor.cpp + src/utils/line_search.cpp + + PUBLIC + include/als.h + include/cals.h + include/tensor.h + include/matrix.h + include/ktensor.h + include/cals_blas.h + include/cuda_utils.h + include/utils/utils.h + include/utils/error.h + include/utils/mttkrp.h + include/utils/update.h + include/multi_ktensor.h + include/utils/line_search.h -SET(CALS_FILES include/timer.h include/definitions.h - src/tensor.cpp include/tensor.h - src/matrix.cpp include/matrix.h - src/ktensor.cpp include/ktensor.h - src/als.cpp include/als.h - src/cals.cpp include/cals.h - src/cuda_utils.cpp include/cuda_utils.h - src/utils.cpp include/utils.h - src/multi_ktensor.cpp include/multi_ktensor.h - include/cals_blas.h src/cals_blas.cpp) - -SET(CALS_EXPERIMENT_FILES - src/experiments/cpu_experiments.cpp src/experiments/experiments_utils.cpp include/experiments/experiments_utils.h - src/experiments/mttkrp_benchmarker.cpp include/experiments/mttkrp_benchmarker.h) - -SET(CALS_MTTKRP_BENCHMARK_FILES - src/experiments/mttkrp_benchmarks.cpp include/experiments/mttkrp_benchmarker.h src/experiments/mttkrp_benchmarker.cpp) - -SET(CALS_EVALUATOR_FILES - src/experiments/peak_evaluator.cpp) - -SET(CALS_CUDA_EXPERIMENT_FILES - src/experiments/cuda_experiment.cpp include/experiments/experiments_utils.h src/experiments/experiments_utils.cpp) + ) -SET(CALS_DRIVER_FILES - src/examples/driver.cpp) +target_compile_definitions(${CP_CALS} PUBLIC ${CP_CALS}=1) # Enable the appropriate BLAS/LAPACK headers in the code -SET(CALS_CUDA_DRIVER_FILES - src/examples/cuda_driver.cpp) +set_target_properties(${CP_CALS} PROPERTIES POSITION_INDEPENDENT_CODE ON) # -fPIC -# CALS Library -SET(CALS_NAME CALS_${BLA_VEN}) -ADD_LIBRARY(${CALS_NAME} SHARED ${CALS_FILES}) +target_include_directories(${CP_CALS} PUBLIC include include/utils) -SET_TARGET_PROPERTIES(${CALS_NAME} PROPERTIES CUDA_SEPARABLE_COMPILATION ON) +target_link_libraries(${CP_CALS} PUBLIC OpenMP::OpenMP_CXX) -foreach (cld ${CALS_LINK_DIRECTORIES}) - TARGET_LINK_DIRECTORIES(${CALS_NAME} PUBLIC ${cld}) -endforeach () -TARGET_LINK_LIBRARIES(${CALS_NAME} ${CALS_LINK_LIBRARIES}) +target_compile_definitions(${CP_CALS} PUBLIC SOURCE_DIR="${CMAKE_SOURCE_DIR}") -TARGET_INCLUDE_DIRECTORIES(${CALS_NAME} PUBLIC include ${CALS_INCLUDE_DIRS} ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}) - -TARGET_COMPILE_DEFINITIONS(${CALS_NAME} PUBLIC ${CALS_NAME}=1) -TARGET_COMPILE_OPTIONS(${CALS_NAME} PUBLIC -fPIC) - -if (${BLA_VEN} STREQUAL "Matlab") - TARGET_COMPILE_DEFINITIONS(${CALS_NAME} PUBLIC WITH_TIME=0) - TARGET_INCLUDE_DIRECTORIES(${CALS_NAME} PRIVATE ${Matlab_INCLUDE_DIRS}) +# Link to BLAS and LAPACK and enable detailed timing, if not in MATLAB. +if (NOT HAVE_MATLAB) + target_compile_definitions(${CP_CALS} PUBLIC WITH_TIME=1) + target_link_libraries(${CP_CALS} PUBLIC ${BLAS_LIBRARIES}) + target_link_libraries(${CP_CALS} PUBLIC ${LAPACK_LIBRARIES}) else () - TARGET_COMPILE_DEFINITIONS(${CALS_NAME} PUBLIC WITH_TIME=1) + target_include_directories(${CP_CALS} PRIVATE ${Matlab_INCLUDE_DIRS}) + target_compile_definitions(${CP_CALS} PUBLIC WITH_TIME=0) + target_link_libraries(${CP_CALS} PUBLIC ${Matlab_MX_LIBRARY}) + target_link_libraries(${CP_CALS} PUBLIC ${Matlab_MEX_LIBRARY}) endif () -if (${WITH_CUBLAS}) - TARGET_COMPILE_DEFINITIONS(${CALS_NAME} PUBLIC CUDA_ENABLED=1) -endif () +################################ +# Optimization Flags +################################ +# TODO: Use -w and -fcompare-debug-second to eliminate Cyclops warnings during compilation +set(comp_flags -Wall) +if (CMAKE_BUILD_TYPE MATCHES Release) + list(APPEND comp_flags -ffast-math -march=native) -# Other Libs and Executables -if (NOT (${BLA_VEN} STREQUAL "Matlab")) - ADD_EXECUTABLE(Experiment_${BLA_VEN} ${CALS_EXPERIMENT_FILES}) - TARGET_LINK_LIBRARIES(Experiment_${BLA_VEN} ${CALS_NAME}) + if (${BLA_VEN} STREQUAL "FLAME") + list(APPEND comp_flags -Wno-unused-function) + endif () - ADD_EXECUTABLE(mttkrp_benchmark_${BLA_VEN} ${CALS_MTTKRP_BENCHMARK_FILES}) - TARGET_LINK_LIBRARIES(mttkrp_benchmark_${BLA_VEN} ${CALS_NAME}) + if (WITH_DIAGNOSTICS GREATER_EQUAL 1) + list(APPEND comp_flags -g) + endif () - ADD_EXECUTABLE(Evaluator_${BLA_VEN} ${CALS_EVALUATOR_FILES}) - TARGET_LINK_LIBRARIES(Evaluator_${BLA_VEN} ${CALS_NAME}) + if (WITH_DIAGNOSTICS GREATER_EQUAL 2) + if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + list(APPEND comp_flags -Rpass=loop-vectorize -Rpass-missed=loop-vectorize -Rpass-analysis=loop-vectorize) + endif () + endif () +endif () - ADD_EXECUTABLE(CUDA_Experiment_${BLA_VEN} ${CALS_CUDA_EXPERIMENT_FILES}) - TARGET_LINK_LIBRARIES(CUDA_Experiment_${BLA_VEN} ${CALS_NAME}) +add_compilation_flags(${CP_CALS} "${comp_flags}") - ADD_EXECUTABLE(driver_${BLA_VEN} ${CALS_DRIVER_FILES}) - TARGET_LINK_LIBRARIES(driver_${BLA_VEN} ${CALS_NAME}) +################################ +# CUBLAS +################################ - ADD_EXECUTABLE(cuda_driver_${BLA_VEN} ${CALS_CUDA_DRIVER_FILES}) - TARGET_LINK_LIBRARIES(cuda_driver_${BLA_VEN} ${CALS_NAME}) +if (WITH_CUBLAS) + set_property(TARGET ${CP_CALS} PROPERTY CUDA_SEPARABLE_COMPILATION ON) + set_property(TARGET ${CP_CALS} PROPERTY CUDA_ARCHITECTURES 50 52 60 61 70 75 80) + target_compile_definitions(${CP_CALS} PUBLIC CUDA_ENABLED=1) + target_link_libraries(${CP_CALS} PUBLIC CUDA::cudart CUDA::cublas) + + set(CP_CALS_CUDA CALS_${BLA_VEN}_CUDA) + add_library(${CP_CALS_CUDA} SHARED src/utils/khatri_rao.cu) + set_property(TARGET ${CP_CALS_CUDA} PROPERTY POSITION_INDEPENDENT_CODE ON) # -fPIC + set_property(TARGET ${CP_CALS_CUDA} PROPERTY CUDA_SEPARABLE_COMPILATION ON) + set_property(TARGET ${CP_CALS_CUDA} PROPERTY CUDA_ARCHITECTURES 50 52 60 61 70 75 80) + target_link_libraries(${CP_CALS} PUBLIC ${CP_CALS_CUDA}) endif () ################################ # GTest ################################ -if (${WITH_TESTS}) - - FIND_PACKAGE(GTest REQUIRED) +if (WITH_TESTS) + find_package(GTest REQUIRED) enable_testing() - INCLUDE_DIRECTORIES(${GTEST_INCLUDE_DIRS}) - SET(GTEST_LINK_FLAGS ${GTEST_BOTH_LIBRARIES} -lpthread) + include_directories(${GTEST_INCLUDE_DIRS}) + set(GTEST_LINK_FLAGS ${GTEST_BOTH_LIBRARIES} -lpthread) - ADD_SUBDIRECTORY(tests) -endif (${WITH_TESTS}) + add_subdirectory(tests) +endif () ################################ # Matlab ################################ - IF (HAVE_MATLAB) - - ADD_LIBRARY( - cals_matlab_interface_library SHARED - ${CALS_SOURCE_DIR}/matlab/matlab.h - ${CALS_SOURCE_DIR}/matlab/matlab.cpp - ${CALS_SOURCE_DIR}/matlab/matlab_parsing.h - ${CALS_SOURCE_DIR}/matlab/matlab_parsing.cpp) - TARGET_INCLUDE_DIRECTORIES(cals_matlab_interface_library PRIVATE ${Matlab_INCLUDE_DIRS}) - TARGET_LINK_LIBRARIES(cals_matlab_interface_library CALS_Matlab ${Matlab_MEX_LIBRARY} ${Matlab_MX_LIBRARY}) - SET(CALS_MATLAB_LINK_LIBS cals_matlab_interface_library CALS_Matlab) + add_library(cals_matlab_interface_library SHARED + ${CMAKE_SOURCE_DIR}/matlab/matlab.h + ${CMAKE_SOURCE_DIR}/matlab/matlab.cpp + ${CMAKE_SOURCE_DIR}/matlab/matlab_parsing.h + ${CMAKE_SOURCE_DIR}/matlab/matlab_parsing.cpp) + target_include_directories(cals_matlab_interface_library PRIVATE ${Matlab_INCLUDE_DIRS}) + target_link_libraries(cals_matlab_interface_library CALS_MATLAB ${Matlab_MEX_LIBRARY} ${Matlab_MX_LIBRARY}) + set(CALS_MATLAB_LINK_LIBS cals_matlab_interface_library CALS_MATLAB) # Newer versions of CMake have an API version argument to MATLAB_ADD_MEX # which needs to be set to the right value since it defaults to an incorrect # value - SET(CALS_MATLAB_API_VERSION "") - IF (${CMAKE_VERSION} VERSION_GREATER_EQUAL "3.14.0") - SET(CALS_MATLAB_API_VERSION "R2018a") - ENDIF () - - MATLAB_ADD_MEX( - NAME cp_cals_driver - SRC ${CALS_SOURCE_DIR}/matlab/matlab_cp_cals.cpp - ${CALS_MATLAB_API_VERSION}) + set(CALS_MATLAB_API_VERSION "") + if (${CMAKE_VERSION} VERSION_GREATER_EQUAL "3.14.0") + set(CALS_MATLAB_API_VERSION "R2018a") + endif () - TARGET_LINK_LIBRARIES(cp_cals_driver ${CALS_MATLAB_LINK_LIBS}) -ENDIF () + matlab_add_mex(NAME cp_cals_driver + SRC ${CMAKE_SOURCE_DIR}/matlab/matlab_cp_cals.cpp + ${CALS_MATLAB_API_VERSION}) + target_link_libraries(cp_cals_driver ${CALS_MATLAB_LINK_LIBS}) +endif () -# Optimization Flags -if (CMAKE_BUILD_TYPE MATCHES Release) - SET(COMP_FLAGS -Ofast -march=native -Wall -Wno-unused-function) # -Wno-unused-function for BLIS lib warnings - - foreach (flag ${COMP_FLAGS}) - CHECK_CXX_COMPILER_FLAG(${flag} COMPILER_OPT_SUPPORTED${flag}) - if (COMPILER_OPT_SUPPORTED${flag}) - MESSAGE(STATUS "Adding ${flag} flag") - TARGET_COMPILE_OPTIONS(${CALS_NAME} PUBLIC ${flag}) - if (${WITH_MATLAB}) - TARGET_COMPILE_OPTIONS(cals_matlab_interface_library PUBLIC ${flag}) - TARGET_COMPILE_OPTIONS(cp_cals_driver PUBLIC ${flag}) - else() - TARGET_COMPILE_OPTIONS(Experiment_${BLA_VEN} PUBLIC ${flag}) - TARGET_COMPILE_OPTIONS(mttkrp_benchmark_${BLA_VEN} PUBLIC ${flag}) - TARGET_COMPILE_OPTIONS(Evaluator_${BLA_VEN} PUBLIC ${flag}) - TARGET_COMPILE_OPTIONS(CUDA_Experiment_${BLA_VEN} PUBLIC ${flag}) - TARGET_COMPILE_OPTIONS(driver_${BLA_VEN} PUBLIC ${flag}) - TARGET_COMPILE_OPTIONS(cuda_driver_${BLA_VEN} PUBLIC ${flag}) - endif () - else () - MESSAGE(STATUS "The compiler does not support ${flag} flag") - endif () - endforeach () +################################ +# Experiments and examples +################################ +if (NOT HAVE_MATLAB) + add_subdirectory(src/examples) + add_subdirectory(src/experiments) endif () diff --git a/DOXYGEN b/DOXYGEN index a915da4..3bdcb7d 100644 --- a/DOXYGEN +++ b/DOXYGEN @@ -864,7 +864,7 @@ FILE_PATTERNS = *.c \ # be searched for input files as well. # The default value is: NO. -RECURSIVE = NO +RECURSIVE = YES # The EXCLUDE tag can be used to specify files and/or directories that should be # excluded from the INPUT source files. This way you can easily exclude a diff --git a/README.md b/README.md index 1ba9248..20f83c3 100644 --- a/README.md +++ b/README.md @@ -7,15 +7,19 @@ Software for computing the Canonical Polyadic Decomposition (CPD), also known as ## Requirements ### Mandatory -* CMake 3.13.5 or higher -* GNU g++ 8.0 (or any C++ compiler with C++17 support) +* CMake 3.17.5 or higher. + A bash script is provided, to help install CMake 3.17.5 (for Linux) in the `extern` folder and updates the `PATH` environment variable to point to it. Try using it by navigating to the directory where you cloned CALS and running: `source scripts/environment_setup.sh`. Then, using the same terminal session, running `cmake --version` should return version 3.17.5. * OpenMP -* Intel MKL (not required for MATLAB MEX generation) +* BLAS/LAPACK (not required for MATLAB MEX generation). Either of the following libraries has been tested to work: + * Intel MKL + * OpenBLAS ### Optional * CUDA 11 * MATLAB 2019b +CALS has been tested to compile with g++-8, g++-10 and clang-10. + ## Compilation Clone the CP-CALS repo using: @@ -35,6 +39,17 @@ cmake \ make -j 8 all ``` +or the following commands to compile the OpenBLAS version. + +```bash +cd CP-CALS/build +cmake \ +-DCMAKE_BUILD_TYPE=Release \ +-DWITH_OPENBLAS=ON .. + +make -j 8 all +``` + Use the following commands to compile the MKL version with CUDA enabled. ```bash @@ -59,6 +74,12 @@ cmake \ make -j 8 all ``` +## A simple example of using CALS in C++ + ## Executing example code -After compiling, you can run `driver_MKL` for the CPU driver, or `cuda_driver_MKL` for the GPU driver (provided you compiled with CUDA enabled). +`src/examples/driver` contains a demonstration of how to use CALS (The -h flag is supported for a look at possible input arguments). + +### MATLAB examples + +After compiling the MATLAB MEX, one can execute file `matlab/matlab_src/TTB_vs_CALS.m` in MATLAB. This executable performs a comparisson of TensorToolbox and CALS. The user first needs to point MATLAB to the CALS MEX and the Tensor Toolbox source code by editing the first two lines of the file. diff --git a/cmake/utils.cmake b/cmake/utils.cmake new file mode 100644 index 0000000..c4e0890 --- /dev/null +++ b/cmake/utils.cmake @@ -0,0 +1,12 @@ +function(add_compilation_flags name flags_list) + message(STATUS "Adding compilation flags to ${name}") + foreach (flag ${flags_list}) + check_cxx_compiler_flag(${flag} COMPILER_OPT_SUPPORTED${flag}) + if (COMPILER_OPT_SUPPORTED${flag}) + # message(STATUS "Adding ${flag} flag to ${name}") + target_compile_options(${name} PUBLIC ${flag}) + else () + message(WARNING "The compiler does not support ${flag} flag") + endif () + endforeach () +endfunction(add_compilation_flags) diff --git a/data/GPU/lookup_tables/100-100-100/0 b/data/GPU/lookup_tables/100-100-100/0 new file mode 100644 index 0000000..2de12ff --- /dev/null +++ b/data/GPU/lookup_tables/100-100-100/0 @@ -0,0 +1,41 @@ +1 0 +2 1 +3 1 +4 1 +5 1 +6 1 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/100-100-100/1 b/data/GPU/lookup_tables/100-100-100/1 new file mode 100644 index 0000000..0311e9b --- /dev/null +++ b/data/GPU/lookup_tables/100-100-100/1 @@ -0,0 +1,41 @@ +1 1 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 1 +9 1 +10 1 +11 1 +12 1 +13 1 +14 1 +15 1 +16 1 +17 1 +18 1 +19 1 +20 1 +30 1 +40 1 +50 1 +60 1 +70 1 +80 1 +90 1 +100 1 +200 1 +300 1 +400 1 +500 1 +600 1 +700 1 +800 1 +900 1 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/100-100-100/2 b/data/GPU/lookup_tables/100-100-100/2 new file mode 100644 index 0000000..b39ed8a --- /dev/null +++ b/data/GPU/lookup_tables/100-100-100/2 @@ -0,0 +1,41 @@ +1 0 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/200-200-200/0 b/data/GPU/lookup_tables/200-200-200/0 new file mode 100644 index 0000000..e4a906c --- /dev/null +++ b/data/GPU/lookup_tables/200-200-200/0 @@ -0,0 +1,41 @@ +1 0 +2 1 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/200-200-200/1 b/data/GPU/lookup_tables/200-200-200/1 new file mode 100644 index 0000000..51182ff --- /dev/null +++ b/data/GPU/lookup_tables/200-200-200/1 @@ -0,0 +1,41 @@ +1 1 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 1 +9 1 +10 1 +11 1 +12 1 +13 1 +14 1 +15 1 +16 1 +17 1 +18 1 +19 1 +20 1 +30 1 +40 1 +50 1 +60 1 +70 1 +80 1 +90 1 +100 1 +200 1 +300 1 +400 1 +500 1 +600 1 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/200-200-200/2 b/data/GPU/lookup_tables/200-200-200/2 new file mode 100644 index 0000000..39ecc72 --- /dev/null +++ b/data/GPU/lookup_tables/200-200-200/2 @@ -0,0 +1,41 @@ +1 0 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 1 +9 1 +10 1 +11 1 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 1 +50 1 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/255-281-25/0 b/data/GPU/lookup_tables/255-281-25/0 new file mode 100644 index 0000000..c552fd4 --- /dev/null +++ b/data/GPU/lookup_tables/255-281-25/0 @@ -0,0 +1,41 @@ +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/255-281-25/1 b/data/GPU/lookup_tables/255-281-25/1 new file mode 100644 index 0000000..cf937ee --- /dev/null +++ b/data/GPU/lookup_tables/255-281-25/1 @@ -0,0 +1,41 @@ +1 2 +2 2 +3 1 +4 2 +5 2 +6 2 +7 2 +8 1 +9 2 +10 1 +11 1 +12 1 +13 1 +14 1 +15 1 +16 1 +17 1 +18 1 +19 1 +20 1 +30 1 +40 1 +50 1 +60 1 +70 1 +80 1 +90 1 +100 1 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/255-281-25/2 b/data/GPU/lookup_tables/255-281-25/2 new file mode 100644 index 0000000..e01e9a0 --- /dev/null +++ b/data/GPU/lookup_tables/255-281-25/2 @@ -0,0 +1,41 @@ +1 1 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 1 +9 1 +10 1 +11 1 +12 1 +13 1 +14 1 +15 1 +16 1 +17 1 +18 1 +19 0 +20 1 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 1 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/299-301-41/0 b/data/GPU/lookup_tables/299-301-41/0 new file mode 100644 index 0000000..c552fd4 --- /dev/null +++ b/data/GPU/lookup_tables/299-301-41/0 @@ -0,0 +1,41 @@ +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/299-301-41/1 b/data/GPU/lookup_tables/299-301-41/1 new file mode 100644 index 0000000..91ba762 --- /dev/null +++ b/data/GPU/lookup_tables/299-301-41/1 @@ -0,0 +1,41 @@ +1 2 +2 1 +3 2 +4 1 +5 1 +6 2 +7 2 +8 1 +9 1 +10 1 +11 1 +12 1 +13 1 +14 1 +15 1 +16 2 +17 1 +18 1 +19 1 +20 1 +30 1 +40 1 +50 1 +60 1 +70 1 +80 1 +90 1 +100 1 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/299-301-41/2 b/data/GPU/lookup_tables/299-301-41/2 new file mode 100644 index 0000000..36bbfba --- /dev/null +++ b/data/GPU/lookup_tables/299-301-41/2 @@ -0,0 +1,41 @@ +1 1 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 1 +9 1 +10 1 +11 0 +12 0 +13 1 +14 0 +15 0 +16 1 +17 0 +18 0 +19 0 +20 0 +30 0 +40 1 +50 1 +60 1 +70 1 +80 1 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/300-300-300/0 b/data/GPU/lookup_tables/300-300-300/0 new file mode 100644 index 0000000..c552fd4 --- /dev/null +++ b/data/GPU/lookup_tables/300-300-300/0 @@ -0,0 +1,41 @@ +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/300-300-300/1 b/data/GPU/lookup_tables/300-300-300/1 new file mode 100644 index 0000000..a81640e --- /dev/null +++ b/data/GPU/lookup_tables/300-300-300/1 @@ -0,0 +1,41 @@ +1 1 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 1 +9 1 +10 1 +11 1 +12 1 +13 1 +14 1 +15 1 +16 1 +17 1 +18 1 +19 1 +20 1 +30 1 +40 1 +50 1 +60 1 +70 1 +80 1 +90 1 +100 1 +200 1 +300 1 +400 1 +500 1 +600 1 +700 1 +800 1 +900 1 +1000 1 +2000 0 +3000 0 +4000 1 +5000 0 diff --git a/data/GPU/lookup_tables/300-300-300/2 b/data/GPU/lookup_tables/300-300-300/2 new file mode 100644 index 0000000..64121ff --- /dev/null +++ b/data/GPU/lookup_tables/300-300-300/2 @@ -0,0 +1,41 @@ +1 0 +2 1 +3 1 +4 1 +5 1 +6 0 +7 0 +8 0 +9 1 +10 1 +11 1 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 1 +50 1 +60 1 +70 1 +80 1 +90 1 +100 1 +200 0 +300 0 +400 0 +500 0 +600 0 +700 1 +800 0 +900 0 +1000 0 +2000 1 +3000 0 +4000 0 +5000 1 diff --git a/data/GPU/lookup_tables/405-136-19/0 b/data/GPU/lookup_tables/405-136-19/0 new file mode 100644 index 0000000..c552fd4 --- /dev/null +++ b/data/GPU/lookup_tables/405-136-19/0 @@ -0,0 +1,41 @@ +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/405-136-19/1 b/data/GPU/lookup_tables/405-136-19/1 new file mode 100644 index 0000000..28dd402 --- /dev/null +++ b/data/GPU/lookup_tables/405-136-19/1 @@ -0,0 +1,41 @@ +1 2 +2 2 +3 2 +4 1 +5 2 +6 2 +7 2 +8 2 +9 2 +10 2 +11 2 +12 2 +13 2 +14 2 +15 2 +16 2 +17 2 +18 2 +19 2 +20 2 +30 2 +40 2 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/GPU/lookup_tables/405-136-19/2 b/data/GPU/lookup_tables/405-136-19/2 new file mode 100644 index 0000000..a25952f --- /dev/null +++ b/data/GPU/lookup_tables/405-136-19/2 @@ -0,0 +1,41 @@ +1 1 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 1 +9 1 +10 1 +11 1 +12 1 +13 1 +14 1 +15 1 +16 1 +17 1 +18 1 +19 1 +20 1 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/MKL/lookup_tables/100-100-100/1/0 b/data/MKL/lookup_tables/100-100-100/1/0 index c33a186..ef5df5c 100644 --- a/data/MKL/lookup_tables/100-100-100/1/0 +++ b/data/MKL/lookup_tables/100-100-100/1/0 @@ -1,5 +1,5 @@ -1 1 -2 1 +1 2 +2 0 3 1 4 1 5 1 @@ -25,17 +25,17 @@ 70 1 80 1 90 1 -100 2 +100 1 200 2 300 2 400 2 500 2 600 2 700 2 -800 1 -900 1 -1000 1 -2000 2 -3000 2 -4000 2 -5000 2 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 1 diff --git a/data/MKL/lookup_tables/100-100-100/1/1 b/data/MKL/lookup_tables/100-100-100/1/1 index c33a186..6d42482 100644 --- a/data/MKL/lookup_tables/100-100-100/1/1 +++ b/data/MKL/lookup_tables/100-100-100/1/1 @@ -1,4 +1,4 @@ -1 1 +1 2 2 1 3 1 4 1 @@ -25,16 +25,16 @@ 70 1 80 1 90 1 -100 2 +100 1 200 2 300 2 400 2 500 2 600 2 700 2 -800 1 -900 1 -1000 1 +800 2 +900 2 +1000 2 2000 2 3000 2 4000 2 diff --git a/data/MKL/lookup_tables/100-100-100/1/2 b/data/MKL/lookup_tables/100-100-100/1/2 index 72f7c1a..ce97b3b 100644 --- a/data/MKL/lookup_tables/100-100-100/1/2 +++ b/data/MKL/lookup_tables/100-100-100/1/2 @@ -1,4 +1,4 @@ -1 1 +1 2 2 2 3 0 4 2 @@ -22,20 +22,20 @@ 40 2 50 2 60 2 -70 2 -80 2 -90 2 -100 2 -200 2 -300 2 -400 2 -500 2 -600 2 -700 2 +70 1 +80 1 +90 1 +100 1 +200 0 +300 1 +400 1 +500 1 +600 1 +700 1 800 1 900 1 1000 1 -2000 1 -3000 1 -4000 1 -5000 2 +2000 2 +3000 2 +4000 2 +5000 1 diff --git a/data/MKL/lookup_tables/100-100-100/12/0 b/data/MKL/lookup_tables/100-100-100/12/0 index 396b608..607a450 100644 --- a/data/MKL/lookup_tables/100-100-100/12/0 +++ b/data/MKL/lookup_tables/100-100-100/12/0 @@ -1,4 +1,4 @@ -1 2 +1 0 2 2 3 2 4 2 diff --git a/data/MKL/lookup_tables/100-100-100/12/1 b/data/MKL/lookup_tables/100-100-100/12/1 index 5beae30..ae2cd26 100644 --- a/data/MKL/lookup_tables/100-100-100/12/1 +++ b/data/MKL/lookup_tables/100-100-100/12/1 @@ -1,4 +1,4 @@ -1 1 +1 0 2 1 3 1 4 1 @@ -12,13 +12,13 @@ 12 1 13 1 14 1 -15 1 +15 0 16 1 -17 1 +17 0 18 1 19 1 -20 1 -30 1 +20 0 +30 0 40 0 50 0 60 0 diff --git a/data/MKL/lookup_tables/100-100-100/12/2 b/data/MKL/lookup_tables/100-100-100/12/2 index 396b608..801129e 100644 --- a/data/MKL/lookup_tables/100-100-100/12/2 +++ b/data/MKL/lookup_tables/100-100-100/12/2 @@ -1,5 +1,5 @@ -1 2 -2 2 +1 0 +2 0 3 2 4 2 5 2 diff --git a/data/MKL/lookup_tables/100-100-100/24/0 b/data/MKL/lookup_tables/100-100-100/24/0 index 396b608..9ed79d0 100644 --- a/data/MKL/lookup_tables/100-100-100/24/0 +++ b/data/MKL/lookup_tables/100-100-100/24/0 @@ -1,4 +1,4 @@ -1 2 +1 0 2 2 3 2 4 2 @@ -18,8 +18,8 @@ 18 2 19 2 20 2 -30 2 -40 2 +30 0 +40 0 50 2 60 2 70 2 @@ -37,5 +37,5 @@ 1000 2 2000 2 3000 2 -4000 2 +4000 0 5000 2 diff --git a/data/MKL/lookup_tables/100-100-100/24/2 b/data/MKL/lookup_tables/100-100-100/24/2 index 396b608..12abd52 100644 --- a/data/MKL/lookup_tables/100-100-100/24/2 +++ b/data/MKL/lookup_tables/100-100-100/24/2 @@ -1,28 +1,28 @@ -1 2 -2 2 -3 2 +1 0 +2 0 +3 0 4 2 5 2 6 2 7 2 -8 2 +8 0 9 2 10 2 11 2 12 2 13 2 -14 2 +14 0 15 2 16 2 17 2 18 2 19 2 20 2 -30 2 -40 2 -50 2 +30 0 +40 0 +50 0 60 2 -70 2 +70 0 80 2 90 2 100 2 diff --git a/data/MKL/lookup_tables/200-200-200/1/0 b/data/MKL/lookup_tables/200-200-200/1/0 index b181a21..b4dbfc8 100644 --- a/data/MKL/lookup_tables/200-200-200/1/0 +++ b/data/MKL/lookup_tables/200-200-200/1/0 @@ -1,10 +1,10 @@ -1 1 -2 2 -3 1 -4 1 +1 0 +2 0 +3 2 +4 2 5 1 6 1 -7 1 +7 0 8 1 9 2 10 2 @@ -27,7 +27,7 @@ 90 2 100 2 200 2 -300 2 +300 0 400 2 500 1 600 1 diff --git a/data/MKL/lookup_tables/200-200-200/1/1 b/data/MKL/lookup_tables/200-200-200/1/1 index 92917c8..542c111 100644 --- a/data/MKL/lookup_tables/200-200-200/1/1 +++ b/data/MKL/lookup_tables/200-200-200/1/1 @@ -1,5 +1,5 @@ 1 1 -2 1 +2 2 3 2 4 2 5 2 @@ -21,9 +21,9 @@ 30 2 40 2 50 2 -60 2 +60 1 70 1 -80 2 +80 1 90 2 100 2 200 2 @@ -32,10 +32,10 @@ 500 2 600 2 700 2 -800 2 -900 2 +800 1 +900 1 1000 2 2000 2 3000 2 -4000 2 +4000 1 5000 2 diff --git a/data/MKL/lookup_tables/200-200-200/1/2 b/data/MKL/lookup_tables/200-200-200/1/2 index ed531c8..1fe4bb1 100644 --- a/data/MKL/lookup_tables/200-200-200/1/2 +++ b/data/MKL/lookup_tables/200-200-200/1/2 @@ -1,10 +1,10 @@ 1 0 -2 1 +2 2 3 1 4 1 5 1 6 1 -7 1 +7 0 8 1 9 1 10 1 @@ -18,22 +18,22 @@ 18 1 19 1 20 1 -30 2 -40 2 -50 2 -60 2 +30 1 +40 1 +50 1 +60 1 70 2 -80 2 -90 2 -100 2 -200 1 -300 1 -400 1 -500 1 -600 1 +80 1 +90 1 +100 1 +200 0 +300 0 +400 0 +500 0 +600 0 700 1 -800 2 -900 2 +800 1 +900 1 1000 1 2000 1 3000 1 diff --git a/data/MKL/lookup_tables/200-200-200/12/0 b/data/MKL/lookup_tables/200-200-200/12/0 index 396b608..607a450 100644 --- a/data/MKL/lookup_tables/200-200-200/12/0 +++ b/data/MKL/lookup_tables/200-200-200/12/0 @@ -1,4 +1,4 @@ -1 2 +1 0 2 2 3 2 4 2 diff --git a/data/MKL/lookup_tables/200-200-200/12/1 b/data/MKL/lookup_tables/200-200-200/12/1 index c552fd4..ad8831e 100644 --- a/data/MKL/lookup_tables/200-200-200/12/1 +++ b/data/MKL/lookup_tables/200-200-200/12/1 @@ -1,6 +1,6 @@ 1 0 -2 0 -3 0 +2 2 +3 2 4 0 5 0 6 0 diff --git a/data/MKL/lookup_tables/200-200-200/12/2 b/data/MKL/lookup_tables/200-200-200/12/2 index 396b608..8eca011 100644 --- a/data/MKL/lookup_tables/200-200-200/12/2 +++ b/data/MKL/lookup_tables/200-200-200/12/2 @@ -1,7 +1,7 @@ 1 2 2 2 3 2 -4 2 +4 0 5 2 6 2 7 2 diff --git a/data/MKL/lookup_tables/200-200-200/24/0 b/data/MKL/lookup_tables/200-200-200/24/0 index 396b608..8ec93a2 100644 --- a/data/MKL/lookup_tables/200-200-200/24/0 +++ b/data/MKL/lookup_tables/200-200-200/24/0 @@ -15,7 +15,7 @@ 15 2 16 2 17 2 -18 2 +18 0 19 2 20 2 30 2 diff --git a/data/MKL/lookup_tables/200-200-200/24/2 b/data/MKL/lookup_tables/200-200-200/24/2 index 396b608..e3d1fda 100644 --- a/data/MKL/lookup_tables/200-200-200/24/2 +++ b/data/MKL/lookup_tables/200-200-200/24/2 @@ -1,8 +1,8 @@ 1 2 2 2 -3 2 +3 0 4 2 -5 2 +5 0 6 2 7 2 8 2 @@ -18,8 +18,8 @@ 18 2 19 2 20 2 -30 2 -40 2 +30 0 +40 0 50 2 60 2 70 2 diff --git a/data/MKL/lookup_tables/255-281-25/1/0 b/data/MKL/lookup_tables/255-281-25/1/0 new file mode 100644 index 0000000..fffd564 --- /dev/null +++ b/data/MKL/lookup_tables/255-281-25/1/0 @@ -0,0 +1,41 @@ +1 1 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 1 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 2 +50 2 +60 2 +70 2 +80 2 +90 2 +100 2 +200 2 +300 2 +400 2 +500 2 +600 2 +700 2 +800 2 +900 2 +1000 2 +2000 2 +3000 2 +4000 2 +5000 2 diff --git a/data/MKL/lookup_tables/255-281-25/1/1 b/data/MKL/lookup_tables/255-281-25/1/1 new file mode 100644 index 0000000..32ee1a3 --- /dev/null +++ b/data/MKL/lookup_tables/255-281-25/1/1 @@ -0,0 +1,41 @@ +1 1 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 1 +9 2 +10 2 +11 2 +12 2 +13 2 +14 2 +15 2 +16 2 +17 2 +18 2 +19 2 +20 2 +30 2 +40 2 +50 2 +60 2 +70 2 +80 2 +90 2 +100 2 +200 2 +300 2 +400 2 +500 2 +600 2 +700 0 +800 0 +900 0 +1000 0 +2000 2 +3000 2 +4000 2 +5000 2 diff --git a/data/MKL/lookup_tables/255-281-25/1/2 b/data/MKL/lookup_tables/255-281-25/1/2 new file mode 100644 index 0000000..6e3ad17 --- /dev/null +++ b/data/MKL/lookup_tables/255-281-25/1/2 @@ -0,0 +1,41 @@ +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 1 +10 1 +11 1 +12 1 +13 1 +14 1 +15 2 +16 1 +17 1 +18 2 +19 2 +20 2 +30 2 +40 2 +50 2 +60 2 +70 2 +80 2 +90 2 +100 2 +200 1 +300 1 +400 1 +500 1 +600 2 +700 2 +800 2 +900 2 +1000 2 +2000 1 +3000 1 +4000 1 +5000 1 diff --git a/data/MKL/lookup_tables/255-281-25/24/0 b/data/MKL/lookup_tables/255-281-25/24/0 new file mode 100644 index 0000000..03d2ad9 --- /dev/null +++ b/data/MKL/lookup_tables/255-281-25/24/0 @@ -0,0 +1,41 @@ +1 2 +2 2 +3 2 +4 2 +5 2 +6 2 +7 2 +8 2 +9 0 +10 2 +11 2 +12 2 +13 2 +14 2 +15 2 +16 0 +17 2 +18 0 +19 2 +20 2 +30 2 +40 2 +50 2 +60 0 +70 0 +80 0 +90 0 +100 0 +200 2 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/MKL/lookup_tables/255-281-25/24/1 b/data/MKL/lookup_tables/255-281-25/24/1 new file mode 100644 index 0000000..c552fd4 --- /dev/null +++ b/data/MKL/lookup_tables/255-281-25/24/1 @@ -0,0 +1,41 @@ +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/MKL/lookup_tables/255-281-25/24/2 b/data/MKL/lookup_tables/255-281-25/24/2 new file mode 100644 index 0000000..607a450 --- /dev/null +++ b/data/MKL/lookup_tables/255-281-25/24/2 @@ -0,0 +1,41 @@ +1 0 +2 2 +3 2 +4 2 +5 2 +6 2 +7 2 +8 2 +9 2 +10 2 +11 2 +12 2 +13 2 +14 2 +15 2 +16 2 +17 2 +18 2 +19 2 +20 2 +30 2 +40 2 +50 2 +60 2 +70 2 +80 2 +90 2 +100 2 +200 2 +300 2 +400 2 +500 2 +600 2 +700 2 +800 2 +900 2 +1000 2 +2000 2 +3000 2 +4000 2 +5000 2 diff --git a/data/MKL/lookup_tables/299-301-41/1/0 b/data/MKL/lookup_tables/299-301-41/1/0 index a71bbee..8c9dea6 100644 --- a/data/MKL/lookup_tables/299-301-41/1/0 +++ b/data/MKL/lookup_tables/299-301-41/1/0 @@ -1,40 +1,41 @@ -1 1 0 -2 1 0 -3 1 0 -4 0 0 -5 0 0 -6 0 0 -7 0 0 -8 0 0 -9 0 0 -10 1 1 -11 1 1 -12 1 1 -13 1 1 -14 1 1 -15 1 1 -16 1 1 -17 1 1 -18 1 1 -19 0 0 -20 0 0 -30 1 1 -40 1 1 -50 1 1 -60 1 1 -70 1 1 -80 1 1 -90 1 1 -100 1 1 -200 1 1 -300 1 1 -400 1 1 -500 1 1 -600 1 1 -700 1 1 -800 1 1 -900 1 1 -1000 1 1 -2000 0 0 -3000 1 1 -4000 1 1 +1 1 +2 1 +3 1 +4 1 +5 1 +6 1 +7 1 +8 1 +9 1 +10 1 +11 1 +12 1 +13 1 +14 2 +15 2 +16 2 +17 0 +18 0 +19 0 +20 0 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 2 +400 2 +500 2 +600 2 +700 2 +800 2 +900 2 +1000 2 +2000 2 +3000 2 +4000 2 +5000 2 diff --git a/data/MKL/lookup_tables/299-301-41/1/1 b/data/MKL/lookup_tables/299-301-41/1/1 index a153989..254cfd6 100644 --- a/data/MKL/lookup_tables/299-301-41/1/1 +++ b/data/MKL/lookup_tables/299-301-41/1/1 @@ -1,40 +1,41 @@ -1 1 0 -2 1 0 -3 0 0 -4 1 0 -5 1 0 -6 1 0 -7 1 0 -8 0 0 -9 1 1 -10 1 1 -11 1 1 -12 1 1 -13 1 1 -14 1 1 -15 1 1 -16 1 1 -17 1 1 -18 1 1 -19 1 1 -20 1 1 -30 1 1 -40 1 1 -50 1 1 -60 1 1 -70 1 1 -80 1 1 -90 1 1 -100 1 1 -200 1 1 -300 1 1 -400 1 1 -500 1 1 -600 1 1 -700 1 1 -800 1 1 -900 1 1 -1000 0 0 -2000 1 1 -3000 1 1 -4000 1 1 +1 1 +2 1 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 2 +18 2 +19 0 +20 2 +30 2 +40 2 +50 2 +60 2 +70 2 +80 2 +90 2 +100 2 +200 2 +300 2 +400 2 +500 2 +600 2 +700 2 +800 2 +900 0 +1000 0 +2000 2 +3000 2 +4000 2 +5000 2 diff --git a/data/MKL/lookup_tables/299-301-41/1/2 b/data/MKL/lookup_tables/299-301-41/1/2 index 868affe..c22bd18 100644 --- a/data/MKL/lookup_tables/299-301-41/1/2 +++ b/data/MKL/lookup_tables/299-301-41/1/2 @@ -1,40 +1,41 @@ -1 0 0 -2 1 1 -3 1 0 -4 0 0 -5 0 0 -6 1 1 -7 1 1 -8 1 0 -9 1 1 -10 1 1 -11 1 1 -12 1 1 -13 1 1 -14 1 1 -15 1 1 -16 1 1 -17 1 1 -18 1 1 -19 1 1 -20 1 0 -30 1 1 -40 1 1 -50 1 1 -60 1 1 -70 1 1 -80 1 1 -90 1 1 -100 1 1 -200 1 0 -300 1 0 -400 1 0 -500 1 1 -600 1 1 -700 1 1 -800 1 0 -900 1 0 -1000 1 0 -2000 1 0 -3000 1 0 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c343fd6..607a450 100644 --- a/data/MKL/lookup_tables/299-301-41/12/2 +++ b/data/MKL/lookup_tables/299-301-41/12/2 @@ -1,40 +1,41 @@ -1 1 1 -2 1 1 -3 1 1 -4 1 1 -5 1 1 -6 1 1 -7 1 1 -8 1 1 -9 1 1 -10 1 1 -11 1 1 -12 1 1 -13 1 1 -14 1 1 -15 1 1 -16 1 1 -17 1 1 -18 1 1 -19 1 1 -20 1 1 -30 1 1 -40 1 1 -50 1 1 -60 1 1 -70 1 1 -80 1 1 -90 1 1 -100 1 1 -200 1 1 -300 1 1 -400 1 1 -500 1 1 -600 1 1 -700 1 1 -800 1 1 -900 1 1 -1000 1 1 -2000 1 1 -3000 1 1 -4000 1 1 +1 0 +2 2 +3 2 +4 2 +5 2 +6 2 +7 2 +8 2 +9 2 +10 2 +11 2 +12 2 +13 2 +14 2 +15 2 +16 2 +17 2 +18 2 +19 2 +20 2 +30 2 +40 2 +50 2 +60 2 +70 2 +80 2 +90 2 +100 2 +200 2 +300 2 +400 2 +500 2 +600 2 +700 2 +800 2 +900 2 +1000 2 +2000 2 +3000 2 +4000 2 +5000 2 diff --git a/data/MKL/lookup_tables/299-301-41/24/0 b/data/MKL/lookup_tables/299-301-41/24/0 index c343fd6..396b608 100644 --- a/data/MKL/lookup_tables/299-301-41/24/0 +++ b/data/MKL/lookup_tables/299-301-41/24/0 @@ -1,40 +1,41 @@ -1 1 1 -2 1 1 -3 1 1 -4 1 1 -5 1 1 -6 1 1 -7 1 1 -8 1 1 -9 1 1 -10 1 1 -11 1 1 -12 1 1 -13 1 1 -14 1 1 -15 1 1 -16 1 1 -17 1 1 -18 1 1 -19 1 1 -20 1 1 -30 1 1 -40 1 1 -50 1 1 -60 1 1 -70 1 1 -80 1 1 -90 1 1 -100 1 1 -200 1 1 -300 1 1 -400 1 1 -500 1 1 -600 1 1 -700 1 1 -800 1 1 -900 1 1 -1000 1 1 -2000 1 1 -3000 1 1 -4000 1 1 +1 2 +2 2 +3 2 +4 2 +5 2 +6 2 +7 2 +8 2 +9 2 +10 2 +11 2 +12 2 +13 2 +14 2 +15 2 +16 2 +17 2 +18 2 +19 2 +20 2 +30 2 +40 2 +50 2 +60 2 +70 2 +80 2 +90 2 +100 2 +200 2 +300 2 +400 2 +500 2 +600 2 +700 2 +800 2 +900 2 +1000 2 +2000 2 +3000 2 +4000 2 +5000 2 diff --git a/data/MKL/lookup_tables/299-301-41/24/1 b/data/MKL/lookup_tables/299-301-41/24/1 index be59b04..c552fd4 100644 --- a/data/MKL/lookup_tables/299-301-41/24/1 +++ b/data/MKL/lookup_tables/299-301-41/24/1 @@ -1,40 +1,41 @@ -1 0 0 -2 0 0 -3 0 0 -4 0 0 -5 0 0 -6 0 0 -7 0 0 -8 0 0 -9 0 0 -10 0 0 -11 0 0 -12 0 0 -13 0 0 -14 0 0 -15 0 0 -16 0 0 -17 0 0 -18 0 0 -19 0 0 -20 0 0 -30 0 0 -40 0 0 -50 0 0 -60 0 0 -70 0 0 -80 0 0 -90 0 0 -100 0 0 -200 0 0 -300 0 0 -400 0 0 -500 0 0 -600 0 0 -700 0 0 -800 0 0 -900 0 0 -1000 0 0 -2000 0 0 -3000 0 0 -4000 0 0 +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 0 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +30 0 +40 0 +50 0 +60 0 +70 0 +80 0 +90 0 +100 0 +200 0 +300 0 +400 0 +500 0 +600 0 +700 0 +800 0 +900 0 +1000 0 +2000 0 +3000 0 +4000 0 +5000 0 diff --git a/data/MKL/lookup_tables/299-301-41/24/2 b/data/MKL/lookup_tables/299-301-41/24/2 index c343fd6..396b608 100644 --- a/data/MKL/lookup_tables/299-301-41/24/2 +++ b/data/MKL/lookup_tables/299-301-41/24/2 @@ -1,40 +1,41 @@ -1 1 1 -2 1 1 -3 1 1 -4 1 1 -5 1 1 -6 1 1 -7 1 1 -8 1 1 -9 1 1 -10 1 1 -11 1 1 -12 1 1 -13 1 1 -14 1 1 -15 1 1 -16 1 1 -17 1 1 -18 1 1 -19 1 1 -20 1 1 -30 1 1 -40 1 1 -50 1 1 -60 1 1 -70 1 1 -80 1 1 -90 1 1 -100 1 1 -200 1 1 -300 1 1 -400 1 1 -500 1 1 -600 1 1 -700 1 1 -800 1 1 -900 1 1 -1000 1 1 -2000 1 1 -3000 1 1 -4000 1 1 +1 2 +2 2 +3 2 +4 2 +5 2 +6 2 +7 2 +8 2 +9 2 +10 2 +11 2 +12 2 +13 2 +14 2 +15 2 +16 2 +17 2 +18 2 +19 2 +20 2 +30 2 +40 2 +50 2 +60 2 +70 2 +80 2 +90 2 +100 2 +200 2 +300 2 +400 2 +500 2 +600 2 +700 2 +800 2 +900 2 +1000 2 +2000 2 +3000 2 +4000 2 +5000 2 diff --git a/data/MKL/lookup_tables/300-300-300/1/0 b/data/MKL/lookup_tables/300-300-300/1/0 index 9ea0839..4d21267 100644 --- a/data/MKL/lookup_tables/300-300-300/1/0 +++ b/data/MKL/lookup_tables/300-300-300/1/0 @@ -1,35 +1,35 @@ -1 1 -2 0 +1 2 +2 2 3 1 4 1 5 1 6 1 7 1 8 1 -9 2 -10 2 -11 2 -12 2 -13 2 -14 2 -15 2 +9 1 +10 1 +11 1 +12 1 +13 1 +14 1 +15 1 16 1 -17 2 -18 2 -19 2 -20 2 -30 2 -40 2 +17 1 +18 1 +19 1 +20 1 +30 1 +40 1 50 2 60 2 -70 2 -80 2 -90 2 +70 0 +80 1 +90 1 100 2 -200 2 +200 1 300 1 400 1 -500 1 +500 2 600 1 700 1 800 1 diff --git a/data/MKL/lookup_tables/300-300-300/1/1 b/data/MKL/lookup_tables/300-300-300/1/1 index 8cecf7d..be120f2 100644 --- a/data/MKL/lookup_tables/300-300-300/1/1 +++ b/data/MKL/lookup_tables/300-300-300/1/1 @@ -1,17 +1,17 @@ -1 0 -2 1 -3 0 -4 0 -5 0 -6 0 +1 1 +2 2 +3 2 +4 2 +5 2 +6 2 7 2 8 2 -9 0 +9 2 10 2 -11 2 -12 2 -13 2 -14 2 +11 0 +12 0 +13 0 +14 0 15 2 16 2 17 2 @@ -24,7 +24,7 @@ 60 2 70 1 80 1 -90 2 +90 1 100 2 200 2 300 2 @@ -32,7 +32,7 @@ 500 2 600 2 700 2 -800 2 +800 1 900 2 1000 2 2000 2 diff --git a/data/MKL/lookup_tables/300-300-300/1/2 b/data/MKL/lookup_tables/300-300-300/1/2 index 51dda2a..3acee73 100644 --- a/data/MKL/lookup_tables/300-300-300/1/2 +++ b/data/MKL/lookup_tables/300-300-300/1/2 @@ -7,29 +7,29 @@ 7 0 8 0 9 0 -10 0 -11 0 -12 0 -13 0 +10 1 +11 1 +12 1 +13 1 14 0 15 0 16 0 -17 0 -18 0 +17 1 +18 1 19 0 -20 0 -30 2 -40 2 +20 1 +30 1 +40 1 50 2 60 2 -70 1 -80 1 +70 2 +80 2 90 1 100 1 200 1 300 1 400 1 -500 1 +500 0 600 1 700 1 800 1 diff --git a/data/MKL/lookup_tables/300-300-300/12/2 b/data/MKL/lookup_tables/300-300-300/12/2 index df6bbd7..50e810b 100644 --- a/data/MKL/lookup_tables/300-300-300/12/2 +++ b/data/MKL/lookup_tables/300-300-300/12/2 @@ -1,5 +1,5 @@ 1 2 -2 2 +2 0 3 0 4 0 5 0 diff --git a/data/MKL/lookup_tables/300-300-300/24/0 b/data/MKL/lookup_tables/300-300-300/24/0 index 96e7c5c..274dea4 100644 --- a/data/MKL/lookup_tables/300-300-300/24/0 +++ b/data/MKL/lookup_tables/300-300-300/24/0 @@ -1,4 +1,4 @@ -1 2 +1 0 2 2 3 2 4 2 @@ -6,9 +6,9 @@ 6 2 7 2 8 2 -9 2 -10 2 -11 2 +9 0 +10 0 +11 0 12 0 13 0 14 0 @@ -16,16 +16,16 @@ 16 0 17 0 18 0 -19 2 -20 2 -30 0 -40 0 -50 2 +19 0 +20 0 +30 2 +40 2 +50 0 60 2 -70 2 -80 2 -90 2 -100 2 +70 0 +80 0 +90 0 +100 0 200 2 300 2 400 2 diff --git a/data/MKL/lookup_tables/300-300-300/24/2 b/data/MKL/lookup_tables/300-300-300/24/2 index 94e0e64..7bc40ed 100644 --- a/data/MKL/lookup_tables/300-300-300/24/2 +++ b/data/MKL/lookup_tables/300-300-300/24/2 @@ -1,5 +1,5 @@ 1 2 -2 2 +2 0 3 0 4 2 5 2 @@ -19,7 +19,7 @@ 19 2 20 2 30 2 -40 2 +40 0 50 2 60 2 70 2 diff --git a/data/MKL/lookup_tables/405-136-19/1/0 b/data/MKL/lookup_tables/405-136-19/1/0 new file mode 100644 index 0000000..cd23315 --- /dev/null +++ b/data/MKL/lookup_tables/405-136-19/1/0 @@ -0,0 +1,41 @@ +1 0 +2 0 +3 2 +4 2 +5 2 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a/data/MKL/lookup_tables/50-50-50/1/0 b/data/MKL/lookup_tables/50-50-50/1/0 deleted file mode 100644 index 4a2e474..0000000 --- a/data/MKL/lookup_tables/50-50-50/1/0 +++ /dev/null @@ -1,40 +0,0 @@ -1 1 0 -2 1 0 -3 1 0 -4 1 0 -5 1 0 -6 1 0 -7 1 1 -8 1 1 -9 1 0 -10 1 0 -11 1 0 -12 1 0 -13 1 0 -14 1 0 -15 1 0 -16 1 0 -17 1 0 -18 1 0 -19 1 0 -20 1 0 -30 1 0 -40 1 0 -50 1 0 -60 1 0 -70 1 0 -80 1 0 -90 1 0 -100 1 0 -200 1 1 -300 1 0 -400 1 1 -500 1 1 -600 1 1 -700 1 1 -800 1 1 -900 1 1 -1000 1 1 -2000 1 1 -3000 1 1 -4000 1 1 diff --git a/data/MKL/lookup_tables/50-50-50/1/1 b/data/MKL/lookup_tables/50-50-50/1/1 deleted file mode 100644 index c24bb5e..0000000 --- a/data/MKL/lookup_tables/50-50-50/1/1 +++ /dev/null @@ -1,40 +0,0 @@ -1 1 0 -2 1 0 -3 1 0 -4 1 0 -5 1 0 -6 1 0 -7 1 0 -8 1 0 -9 1 0 -10 1 0 -11 1 0 -12 1 0 -13 1 0 -14 1 0 -15 1 0 -16 1 0 -17 1 0 -18 1 0 -19 1 0 -20 1 0 -30 1 0 -40 1 0 -50 1 0 -60 1 0 -70 1 0 -80 1 0 -90 1 0 -100 1 0 -200 1 1 -300 1 1 -400 1 1 -500 1 1 -600 1 1 -700 1 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0 -6 1 0 -7 1 0 -8 1 0 -9 1 0 -10 1 0 -11 1 0 -12 1 0 -13 1 0 -14 1 0 -15 1 0 -16 1 0 -17 1 0 -18 1 0 -19 1 0 -20 1 0 -30 1 0 -40 1 0 -50 1 0 -60 1 0 -70 1 0 -80 1 0 -90 1 0 -100 1 0 -200 0 0 -300 0 0 -400 0 0 -500 0 0 -600 0 0 -700 0 0 -800 0 0 -900 0 0 -1000 0 0 -2000 0 0 -3000 0 0 -4000 0 0 diff --git a/data/MKL/lookup_tables/50-50-50/24/2 b/data/MKL/lookup_tables/50-50-50/24/2 deleted file mode 100644 index 6b77bf7..0000000 --- a/data/MKL/lookup_tables/50-50-50/24/2 +++ /dev/null @@ -1,40 +0,0 @@ -1 0 0 -2 1 1 -3 1 1 -4 1 1 -5 1 1 -6 1 1 -7 1 1 -8 1 1 -9 1 1 -10 1 1 -11 1 1 -12 1 1 -13 1 1 -14 1 1 -15 1 1 -16 1 1 -17 1 1 -18 1 1 -19 1 1 -20 1 1 -30 1 1 -40 1 1 -50 1 1 -60 1 1 -70 1 1 -80 1 1 -90 1 1 -100 1 1 -200 1 1 -300 1 1 -400 1 1 -500 1 1 -600 1 1 -700 1 1 -800 1 1 -900 1 1 -1000 1 1 -2000 1 1 -3000 1 1 -4000 0 0 diff --git a/data/README.md b/data/README.md index 3511063..b8cb946 100644 --- a/data/README.md +++ b/data/README.md @@ -5,3 +5,10 @@ The dataset contained in the file `fluorescence_cancer_UD.txt` was found online For more information on the above dataset, please refer to: Lawaetz, A.; Bro, R.; Kamstrup-Nielsen, M.; Christensen, I.; Jørgensen, L.; Nielsen, H. Fluorescence spectroscopy as a potential metabonomic tool for early detection of colorectal cancer. Metabolomics 8 (supplement 1): 111-121 (2012). + +The dataset contained in the file `eemdata.txt` was found online [here](http://www.models.life.ku.dk/Fluorescence) (some preprocessing was done to interpolate missing values). + +For more information on the above dataset, please refer to: + +Bro, R., Rinnan, Å., & Faber, N. K. M. (2005). Standard error of prediction for multilinear PLS: 2. Practical implementation in fluorescence spectroscopy. Chemometrics and Intelligent Laboratory Systems, 75(1), 69-76. https://doi.org/10.1016/j.chemolab.2004.04.014 + diff --git a/data/eemdata.txt b/data/eemdata.txt new file mode 100644 index 0000000..b2c0e5b --- /dev/null +++ b/data/eemdata.txt @@ -0,0 +1,1046521 @@ +405 136 19 +7.318338e+00 +6.588402e+00 +9.410869e+00 +6.924955e+00 +1.139054e+01 +6.997697e+00 +8.096931e+00 +8.258579e+00 +7.662397e+00 +8.085280e+00 +7.692034e+00 +8.739293e+00 +7.426160e+00 +6.476391e+00 +7.809556e+00 +7.176038e+00 +7.108032e+00 +7.682115e+00 +7.176038e+00 +7.108032e+00 +6.938101e+00 +6.396039e+00 +7.337488e+00 +7.061651e+00 +7.864633e+00 +6.029795e+00 +7.172675e+00 +7.185390e+00 +7.885710e+00 +6.275239e+00 +7.868203e+00 +8.520945e+00 +7.503073e+00 +7.885489e+00 +8.001023e+00 +8.089840e+00 +5.442747e+00 +7.564112e+00 +8.098561e+00 +7.103177e+00 +7.550401e+00 +7.408351e+00 +7.845493e+00 +7.705997e+00 +7.318913e+00 +7.606686e+00 +7.646133e+00 +6.017678e+00 +6.409682e+00 +6.765429e+00 +7.003056e+00 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+1.117735e-01 +2.744990e-01 +1.516358e-01 +1.554122e-01 +2.273761e-01 diff --git a/scripts/Lookup Table Generator/__init__.py b/data/figures/.gitkeep similarity index 100% rename from scripts/Lookup Table Generator/__init__.py rename to data/figures/.gitkeep diff --git a/extern/.gitkeep b/extern/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/include/als.h b/include/als.h index 8108c84..533b3fd 100644 --- a/include/als.h +++ b/include/als.h @@ -1,146 +1,198 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #ifndef CALS_ALS_H #define CALS_ALS_H #include #include -#include "utils.h" -#include "timer.h" -#include "tensor.h" #include "ktensor.h" +#include "tensor.h" +#include "timer.h" +#include "utils/mttkrp.h" +#include "utils/update.h" +#include "utils/utils.h" + +namespace cals { +/** struct containing information about an ALS execution. + * + */ +struct AlsReport { + // Target tensor details + int tensor_rank; /*!< Target tensor rank (if available). */ + int n_modes; /*!< Target tensor number of modes (dimensions). */ + vector modes; /*!< Target tensor mode sizes. */ + double X_norm; /*!< Target tensor norm. */ + + // Execution parameters + int iter = 0; /*!< Total number of iterations performed by the ALS algorithm. */ + int max_iter; /*!< Maximum number of iterations set by the user. */ + int n_threads; /*!< Number of threads available. */ + int ktensor_id; /*!< ID of the Ktensor that was fitted. */ + int ktensor_rank; /*!< Number of components of the Ktensor that was fitted. */ + double tol; /*!< Tolerance used to determine when a Ktensor has reached convergence. */ + bool cuda; /*!< Whether CUDA was used. */ + bool line_search; /*!< Whether line search was used. */ + int line_search_interval; /*!< Number of iterations (per model) when line search was invoked. */ + double line_search_step; /*!< Factor with which line search moved. */ + update::UPDATE_METHOD update_method; /*!< Update method used for all Ktensors. */ + + // Experiment analysis data + uint64_t flops_per_iteration; /*!< Number of FLOPS performed in each iteration. */ + + // Timers + double total_time{0.0}; +#if WITH_TIME + Matrix als_times{}; + Matrix mode_times{}; + Matrix mttkrp_times{}; +#endif + + /* Print header of CSV file, containing information about an ALS invocation. + * + * @param file_name Name of CSV file. + * @param sep Optional argument, separator of CSV. + * */ + void print_header(const std::string &file_name, const std::string &sep = ";") const { + auto file = std::ofstream(file_name, std::ios::out); + + AlsTimers als_timers; + ModeTimers mode_timers; + file << "TENSOR_RANK" << sep; + file << "TENSOR_MODES" << sep; + file << "KTENSOR_ID" << sep; + file << "KTENSOR_RANK" << sep; + file << "UPDATE_METHOD" << sep; + file << "LINE_SEARCH" << sep; + file << "MAX_ITERS" << sep; + file << "ITER" << sep; + file << "NUM_THREADS" << sep; + file << "FLOPS" << sep; +#if WITH_TIME + file << "TOTAL" << sep; + for (const auto &name : als_timers.names) + file << name << sep; + for (auto i = 0lu; i < modes.size(); i++) + for (auto &name : mode_timers.names) + file << "MODE_" << i << "_" << name << sep; +#endif + file << std::endl; + } + + /* Print contents of CSV file, containing information about an ALS invocation. + * + * @param file_name Name of CSV file. + * @param sep Optional argument, separator of CSV. + * */ + void print_to_file(const std::string &file_name, const std::string &sep = ";") const { + auto file = std::ofstream(file_name, std::ios::app); + + file << tensor_rank << sep; + file << cals::utils::mode_string(modes) << sep; + file << ktensor_id << sep; + file << ktensor_rank << sep; + file << update::update_method_names[update_method] << sep; + file << line_search << sep; + file << max_iter << sep; + file << iter << sep; + file << n_threads << sep; + file << flops_per_iteration << sep; +#if WITH_TIME + file << std::scientific; + file << total_time << sep; -namespace cals -{ - struct AlsReport - { - // Target tensor details - int tensor_rank; - int n_modes; - vector modes; - double X_norm; - - // Execution parameters - int iter = 0; - int max_iter; - int n_threads; - int ktensor_id; - int ktensor_rank; - double tol; - bool cuda; - bool line_search; - int line_search_interval; - double line_search_step{0.9}; - update::UPDATE_METHOD update_method; - - // Execution data - uint64_t flops_per_iteration; - - // Timers - TIME(AlsTimer als_timer{};) - TIME(vector mode_timers;) - - TIME(void init_timers() - { mode_timers.resize(n_modes, ModeTimer()); }) - - void print_header(const std::string &file_name, const std::string &sep = ";") - { - auto file = std::ofstream(file_name, std::ios::out); - file << "TENSOR_RANK" << sep - << "TENSOR_MODES" << sep - << "KTENSOR_ID" << sep - << "KTENSOR_RANK" << sep - << "UPDATE_METHOD" << sep - << "LINE_SEARCH" << sep - << "MAX_ITERS" << sep - << "ITER" << sep - << "NUM_THREADS" << sep - << "FLOPS" << sep; - TIME( for (const auto &name : als_timer.names) - file << name << sep; - for (auto i = 0lu; i < modes.size(); i++) - for (auto j : {ModeTimer::TOTAL_MTTKRP, ModeTimer::UPDATE}) - file << "MODE_" << i << "_" << mode_timers[0].names[j] << sep; - ) - file << std::endl; + auto max_it = static_cast(iter - 1); + + // Find the minimum value per row (min across all iterations) and write it to file + for (dim_t i = 0; i < als_times.get_rows(); i++) { + double min = std::numeric_limits::max(); + for (dim_t j = 0; j < max_it; j++) + if (als_times(i, j) < min) + min = als_times(i, j); + file << min << sep; } - void print_to_file(const std::string &file_name, const std::string &sep = ";") - { - auto file = std::ofstream(file_name, std::ios::app); - - file << tensor_rank << sep; - for (auto m = modes.cbegin(); m < modes.cend() - 1; m++) file << *m << "-"; - file << *(modes.cend() - 1) << sep; - file << ktensor_id << sep; - file << ktensor_rank << sep; - file << update::update_method_names[update_method] << sep; - file << line_search << sep; - file << max_iter << sep; - file << iter << sep; - file << n_threads << sep; - file << flops_per_iteration << sep; - TIME(for (auto &timer : als_timer.timers) - file << timer.get_min() << sep; - for (auto &mode_timer : mode_timers) - for (auto j : {ModeTimer::TOTAL_MTTKRP, ModeTimer::UPDATE}) - file << mode_timer.timers[j].get_min() << sep; - ) - file << std::endl; + // Find the minimum value per row (min across all iterations) and write it to file + for (dim_t i = 0; i < mode_times.get_rows(); i++) { + double min = std::numeric_limits::max(); + for (dim_t j = 0; j < max_it; j++) + if (mode_times(i, j) < min) + min = mode_times(i, j); + file << min << sep; } - }; - - struct AlsParams - { - double tol{1e-4}; - int max_iterations{150}; - ops::MTTKRP_METHOD mttkrp_method{ops::MTTKRP_METHOD::AUTO}; - int mttkrp_twostep_option{0}; - bool force_max_iter{false}; - update::UPDATE_METHOD update_method{update::UPDATE_METHOD::UNCONSTRAINED}; - bool cuda{false}; - bool line_search{false}; - int line_search_interval{5}; - double line_search_step{0.9}; - }; - - AlsReport cp_als(const Tensor &X, Ktensor &ktensor, AlsParams ¶ms); -} - -#endif //CALS_ALS_H +#endif + file << std::endl; + } +}; + +struct AlsParams { + /*! Method for updating factor matrices. */ + update::UPDATE_METHOD update_method{update::UPDATE_METHOD::UNCONSTRAINED}; + + /*! MTTKRP method to use. Look at mttkrp::MTTKRP_METHOD. */ + mttkrp::MTTKRP_METHOD mttkrp_method{mttkrp::MTTKRP_METHOD::AUTO}; + + /*! Lookup table with the best variant of MTTKRP per mode. */ + cals::mttkrp::MttkrpLut mttkrp_lut{}; + + int max_iterations{200}; /*!< Maximum number of iterations before evicting a model. */ + double tol{1e-7}; /*!< Tolerance of fit difference between consecutive iterations. */ + bool line_search{false}; /*!< Use line search. */ + int line_search_interval{5}; /*!< Number of iterations when line search is invoked.*/ + double line_search_step{1.2}; /*!< Factor for line search. */ + bool cuda{false}; /*!< Use CUDA (make sure code is compiled with CUDA support). */ + + // Internal variables (not for use by the users) + bool omp_enabled{false}; /*!< Let ALS know that multiple instances of it are running in parallel. (for OpenMP ALS) */ + + bool force_max_iter{false}; /*!< Force maximum iterations for every model (for experiments) */ + bool suppress_lut_warning{false}; /*!< Suppress the warning that the LookupTable was not found (for experiments) */ + + /** Print contents of AlsParams to standard output. + */ + void print() const { + using std::cout; + using std::endl; + + cout << "---------------------------------------" << endl; + cout << "ALS parameters" << endl; + cout << "---------------------------------------" << endl; + cout << "Tolerance: " << tol << endl; + cout << "Max Iterations: " << max_iterations << endl; + cout << "Mttkrp Method: " << mttkrp::mttkrp_method_names[mttkrp_method] << endl; + cout << "Update Method: " << update::update_method_names[update_method] << endl; + cout << "Line Search: " << ((line_search) ? "true" : "false") << endl; + if (line_search) + cout << "-Line Search Int: " << line_search_interval << " iterations" << endl; + cout << "CUDA: " << ((cuda) ? "true" : "false") << endl; + cout << "---------------------------------------" << endl; + } +}; + +/** Computes a single Alternating Least Squares algorithm, for the Canonical Polyadic Decomposition. + * + * The function fits the \p ktensor to the target tensor \p X using ALS, overwritting the \p ktensor with the result. + * + * @param X Target tensor to be decomposed. + * @param ktensor A Ktensor, which needs to be fitted to the target tensor using ALS. + * @param params Parameters for the algorithm. + * + * @return AlsReport object, containing all data from the execution. + */ +AlsReport cp_als(const Tensor &X, Ktensor &ktensor, AlsParams ¶ms); + +/** Computes multiple Alternating Least Squares algorithms for the Canonical Polyadic Decomposition, in parallel, using + * OpenMP. + * + * The function lets OpenMP create threads and distribute the computation specified in the vector \p ktensor_v. + * Each thread invokes cp_als to fit each input Ktensor to the target tensor \p X using ALS and then overwrites + * the Ktensor with the result. + * + * @param X Target tensor to be decomposed. + * @param ktensor_v A reference to a vector of input Ktensors, which need to be fitted to the target tensor using ALS. + * @param params Parameters for the algorithm. + * + * @return vector of AlsReport objects, containing data from the execution of each als algorithm. + */ +vector cp_omp_als(const Tensor &X, vector &ktensor, AlsParams ¶ms); +} // namespace cals + +#endif // CALS_ALS_H diff --git a/include/cals.h b/include/cals.h index 0af4699..543bd93 100644 --- a/include/cals.h +++ b/include/cals.h @@ -1,222 +1,179 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #ifndef CALS_CALS_H #define CALS_CALS_H -#include #include -#include #include -#include #include +#include +#include +#include -#include "utils.h" -#include "timer.h" -#include "tensor.h" #include "ktensor.h" - -namespace cals -{ - typedef std::queue> KtensorQueue; /* A queue of references to Ktensors, used as input to CALS. */ - - /** struct containing information about a CALS execution. +#include "tensor.h" +#include "timer.h" +#include "utils/mttkrp.h" +#include "utils/update.h" +#include "utils/utils.h" + +namespace cals { +/* A queue of references to Ktensors, used as input to CALS. */ +typedef std::queue> KtensorQueue; + +/** struct containing information about a CALS execution. + * + */ +struct CalsReport { + // Target tensor details + int tensor_rank; /*!< Target tensor rank (if available). */ + int n_modes; /*!< Target tensor number of modes (dimensions). */ + vector modes; /*!< Target tensor mode sizes. */ + double X_norm; /*!< Target tensor norm. */ + + // Execution parameters + int iter{0}; /*!< Total number of iterations of the CALS algorithm. */ + int max_iter; /*!< Maximum number of iterations for each model. */ + int n_threads; /*!< Number of threads available. */ + int buffer_size; /*!< Max buffer size used. */ + int n_ktensors{0}; /*!< Total number of Ktensors that were fitted to the target tensor. */ + int ktensor_rank_sum{0}; /*!< Total rank sum of all Ktensors fitted to the target tensor. */ + double tol; /*!< Tolerance used to determine when a Ktensor had reached convergence. */ + bool cuda; /*!< Whether CUDA was used. */ + bool line_search; /*!< Whether line search was used. */ + int line_search_interval; /*!< Number of iterations (per model) when line search was invoked. */ + double line_search_step; /*!< Factor with which line search moved. */ + update::UPDATE_METHOD update_method; /*!< Update method used for all Ktensors. */ + std::string output_file_name; /*!< Name of the output file to export data (used in experiments) */ + + // Timers + double total_time{0.0}; +#if WITH_TIME + Matrix als_times{}; + Matrix mode_times{}; + Matrix mttkrp_times{}; + + // Experiment analysis data + vector flops_per_iteration; /*!< Number of FLOPS performed in each iteration. */ + vector cols; /*!< Number of active columns of the multi-factors for every iteration. */ +#endif + + /* Print header of CSV file, containing information about an CALS invocation. * - */ - struct CalsReport - { - // Target tensor details - int tensor_rank; /*!< Target tensor rank (if available). */ - int n_modes; /*!< Target tensor number of modes (dimensions). */ - vector modes; /*!< Target tensor mode sizes. */ - double X_norm; /*!< Target tensor norm. */ - - // Execution parameters - int iter{0}; /*!< Total number of iterations of the CALS algorithm. */ - int max_iter; /*!< Maximum number of iterations for each model. */ - int n_threads; /*!< Number of threads available. */ - int buffer_size; /*!< Max buffer size used. */ - int n_ktensors{0}; /*!< Total number of Ktensors that were fitted to the target tensor. */ - int ktensor_rank_sum{0}; /*!< Total rank sum of all Ktensors fitted to the target tensor. */ - double tol; /*!< Tolerance used to determine when a Ktensor has reached convergence. */ - bool cuda; /*!< Whether to use CUDA (make sure that the code is compiled with CUDA support). */ - bool line_search; /*!< Whether to use line search. */ - int line_search_interval; /*!< Number of iterations (per model) for invocation of line search. */ - double line_search_step; /*!< Factor with which to move during line search. */ - update::UPDATE_METHOD update_method; /*!< Update method used for all Ktensors. */ - std::string output_file_name; /*!< Name of the output file to export data (used in experiments) */ - - // Execution data - TIME(vector flops_per_iteration;) /*!< Number of FLOPS performed in each iteration. */ - TIME(vector cols;) /*!< Number of active columns of the multi-factors for every iteration. */ - - // Timers - TIME(vector als_timer;) /*!< Timers for anything outside of MTTKRP. */ - TIME(vector> mode_timers;) /*!< Timers for MTTKRP (per mode). */ - - /* Function to initialize timers - * */ - TIME(void init_timers(int n_modes, int max_iter) - { - mode_timers.resize(max_iter, vector(n_modes, ModeTimer())); - als_timer.resize(max_iter, AlsTimer()); - flops_per_iteration.resize(max_iter, 0llu); - cols.resize(max_iter, 0); - } - ) - - /* Print header of CSV file. - * - * @param file_name Name of CSV file. - * @param sep Optional argument, separator of CSV. - * */ - void print_header(const std::string &file_name, const std::string &sep = ";") - { - auto file = std::ofstream(file_name, std::ios::out); - file << "TENSOR_RANK" << sep - << "TENSOR_MODES" << sep - << "BUFFER_SIZE" << sep - << "N_KTENSORS" << sep - << "KTENSOR_RANK_SUM" << sep - << "UPDATE_METHOD" << sep - << "LINE_SEARCH" << sep - << "MAX_ITERS" << sep - << "ITER" << sep - << "NUM_THREADS" << sep; - TIME(file << "FLOPS" << sep - << "COLS" << sep; - for (const auto &name : als_timer[0].names) - file << name << sep; - for (auto i = 0lu; i < modes.size(); i++) - for (auto j : {ModeTimer::TOTAL_MTTKRP, ModeTimer::UPDATE}) - file << "MODE_" << i << "_" << mode_timers[0][0].names[j] << sep; - ) - file << std::endl; - } - - /* Print contents of CSV file. - * - * @param file_name Name of CSV file. - * @param sep Optional argument, separator of CSV. - * */ - void print_to_file(const std::string &file_name, const std::string &sep = ";") - { - auto file = std::ofstream(file_name, std::ios::app); - - for (auto it = 1; it <= iter; it++) - { - file << tensor_rank << sep; - for (auto m = modes.cbegin(); m < modes.cend() - 1; m++) file << *m << "-"; - file << *(modes.cend() - 1) << sep; - file << buffer_size << sep; - file << n_ktensors << sep; - file << ktensor_rank_sum << sep; - file << update::update_method_names[update_method] << sep; - file << line_search << sep; - file << max_iter << sep; - file << it << sep; - file << n_threads << sep; - TIME(file << flops_per_iteration[it] << sep; - file << cols[it] << sep; - for (auto &timer : als_timer[it].timers) - file << timer.get_time() << sep; - for (auto &mode_timer : mode_timers[it]) - for (auto j : {ModeTimer::TOTAL_MTTKRP, ModeTimer::UPDATE}) - file << mode_timer.timers[j].get_min() << sep; - ) - file << std::endl; - } - } - }; - - /** Struct containing all the execution parameters of the CALS algorithm. + * @param file_name Name of CSV file. + * @param sep Optional argument, separator of CSV. + * */ + void print_header(const std::string &file_name, const std::string &sep = ";") const { + auto file = std::ofstream(file_name, std::ios::out); + + AlsTimers als_timers; + ModeTimers mode_timers; + file << "TENSOR_RANK" << sep << "TENSOR_MODES" << sep << "BUFFER_SIZE" << sep << "N_KTENSORS" << sep + << "KTENSOR_RANK_SUM" << sep << "UPDATE_METHOD" << sep << "LINE_SEARCH" << sep << "MAX_ITERS" << sep << "ITER" + << sep << "NUM_THREADS" << sep; +#if WITH_TIME + file << "TOTAL" << sep << "FLOPS" << sep << "COLS" << sep; + for (const auto &name : als_timers.names) + file << name << sep; + for (auto i = 0lu; i < modes.size(); i++) + for (auto &name : mode_timers.names) + file << "MODE_" << i << "_" << name << sep; +#endif + file << std::endl; + } + + /* Print contents of CSV file, containing information about an CALS invocation. * - */ - struct CalsParams - { - update::UPDATE_METHOD - update_method{update::UPDATE_METHOD::UNCONSTRAINED}; /*!< Method for updating factor matrices. */ - ops::MTTKRP_METHOD - mttkrp_method{ops::MTTKRP_METHOD::AUTO}; /*!< MTTKRP method to use. Look at ops::MTTKRP_METHOD. */ - int max_iterations{50}; /*!< Maximum number of iterations before evicting a model. */ - int buffer_size{4200}; /*!< Maximum size of the multi-factor columns (sum of ranks of concurrent models) */ - double tol{1e-4}; /*!< Tolerance of fit difference between consecutive iterations. */ - - bool cuda{false}; /*!< Use CUDA (make sure code is compiled with CUDA support). */ - - bool line_search{false}; /*!< Use line search. */ - int line_search_interval{5}; /*!< Number of iterations when line search is invoked.*/ - double line_search_step{1.2}; /*!< Factor for line search. */ - - bool force_max_iter{false}; /*!< Force maximum iterations for every model (for experiments) */ - - /** Print contents of CalsParams to standard output. - */ - void print() const - { - using std::cout; - using std::endl; - - cout << "---------------------------------------" << endl; - cout << "CALS parameters" << endl; - cout << "---------------------------------------" << endl; - cout << "Tol: " << tol << endl; - cout << "Max Iterations: " << max_iterations << endl; - cout << "Buffer Size: " << buffer_size << endl; - cout << "Mttkrp Method: " << mttkrp_method << endl; - cout << "Update Method: " << update_method << endl; - cout << "Line Search: " << line_search << endl; - cout << "Line Search Int: " << line_search_interval << endl; - cout << "CUDA: " << cuda << endl; - cout << "---------------------------------------" << endl; + * @param file_name Name of CSV file. + * @param sep Optional argument, separator of CSV. + * */ + void print_to_file(const std::string &file_name, const std::string &sep = ";") const { + auto file = std::ofstream(file_name, std::ios::app); + auto max_it = static_cast(iter - 1); + + for (dim_t it = 0; it <= max_it; it++) { + file << tensor_rank << sep; + file << cals::utils::mode_string(modes) << sep; + file << buffer_size << sep; + file << n_ktensors << sep; + file << ktensor_rank_sum << sep; + file << update::update_method_names[update_method] << sep; + file << line_search << sep; + file << max_iter << sep; + file << it + 1 << sep; + file << n_threads << sep; +#if WITH_TIME + file << total_time << sep; + file << flops_per_iteration[it] << sep; + file << cols[it] << sep; + + file << std::scientific; + for (dim_t i = 0; i < als_times.get_rows(); i++) + file << als_times(i, it) << sep; + + for (dim_t i = 0; i < mode_times.get_rows(); i++) + file << mode_times(i, it) << sep; +#endif + file << std::endl; } - }; - - /** Computes multiple Concurrent Alternating Least Squares algorithms, for the Canonical Polyadic Decomposition. - * - * The function consumes Ktensors from the input queue \p kt_queue, fits them to the target tensor \p X using ALS, - * and then overwrites each input Ktensor with the result. - * - * @param X Target tensor to be decomposed. - * @param kt_queue A queue of references to the input Ktensors, which need to be fitted to the target tensor using ALS. - * @param params Parameters for the algorithm. - * - * @return CalsReport object, containing all data from the execution. + } +}; + +/** Struct containing all the execution parameters of the CALS algorithm. + * + */ +struct CalsParams { + /*! Method for updating factor matrices. */ + update::UPDATE_METHOD update_method{update::UPDATE_METHOD::UNCONSTRAINED}; + + /*! MTTKRP method to use. Look at mttkrp::MTTKRP_METHOD. */ + mttkrp::MTTKRP_METHOD mttkrp_method{mttkrp::MTTKRP_METHOD::AUTO}; + + /*! Lookup table with the best variant of MTTKRP per mode. */ + cals::mttkrp::MttkrpLut mttkrp_lut{}; + + dim_t max_iterations{200}; /*!< Maximum number of iterations before evicting a model. */ + double tol{1e-7}; /*!< Tolerance of fit difference between consecutive iterations. */ + bool line_search{false}; /*!< Use line search. */ + int line_search_interval{5}; /*!< Number of iterations when line search is invoked.*/ + double line_search_step{1.2}; /*!< Factor for line search. */ + bool cuda{false}; /*!< Use CUDA (make sure code is compiled with CUDA support). */ + dim_t buffer_size{4200}; /*!< Maximum size of the multi-factor columns (sum of ranks of concurrent models) */ + + bool force_max_iter{false}; /*!< Force maximum iterations for every model (for experiments) */ + bool always_evict_first{false}; /*!< Always evict the first model in the buffer (for experiments) */ + + /** Print contents of CalsParams to standard output. */ - CalsReport cp_cals(const Tensor &X, KtensorQueue &kt_queue, CalsParams ¶ms); -} -#endif //CALS_CALS_H + void print() const { + using std::cout; + using std::endl; + + cout << "---------------------------------------" << endl; + cout << "CALS parameters" << endl; + cout << "---------------------------------------" << endl; + cout << "Tol: " << tol << endl; + cout << "Max Iterations: " << max_iterations << endl; + cout << "Buffer Size: " << buffer_size << endl; + cout << "Mttkrp Method: " << mttkrp::mttkrp_method_names[mttkrp_method] << endl; + cout << "Update Method: " << update::update_method_names[update_method] << endl; + cout << "Line Search: " << ((line_search) ? "true" : "false") << endl; + if (line_search) + cout << "-Line Search Int: " << line_search_interval << " iterations" << endl; + cout << "CUDA: " << ((cuda) ? "true" : "false") << endl; + cout << "---------------------------------------" << endl; + } +}; + +/** Computes multiple Concurrent Alternating Least Squares algorithms, for the Canonical Polyadic Decomposition. + * + * The function consumes Ktensors from the input queue \p kt_queue, fits them to the target tensor \p X using ALS, + * and then overwrites each input Ktensor with the result. + * + * @param X Target tensor to be decomposed. + * @param kt_queue A queue of references to the input Ktensors, which need to be fitted to the target tensor using ALS. + * @param params Parameters for the algorithm. + * + * @return CalsReport object, containing all data from the execution. + */ +CalsReport cp_cals(const Tensor &X, KtensorQueue &kt_queue, CalsParams ¶ms); +} // namespace cals +#endif // CALS_CALS_H diff --git a/include/cals_blas.h b/include/cals_blas.h index f76fb40..40c121e 100644 --- a/include/cals_blas.h +++ b/include/cals_blas.h @@ -1,145 +1,153 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #ifndef CALS_CALS_BLAS_H #define CALS_CALS_BLAS_H +#include "omp.h" + #if CALS_MKL + #include "mkl.h" #define CALS_BACKEND "MKL" -#elif CALS_BLIS + +#elif CALS_FLAME + #include "blis.h" #include "mkl_lapack.h" #define CALS_BACKEND "BLIS" + #elif CALS_OPENBLAS + #include "cblas.h" -#include "mkl_lapack.h" + #define CALS_BACKEND "OPENBLAS" -#elif CALS_Matlab -#include "mex.h" +extern "C" { +void dposv_(const char *, + const long int *, + const long int *, + double *, + const long int *, + double *, + const long int *, + long int *); +void dpotrf_(const char *, const long int *, double *, const long int *, long int *); +}; + +inline void dposv(const char *uplo, + int const *N, + int const *Rhs, + double *A, + int const *Lda, + double *B, + int const *Ldb, + int const *Info) { + long int const n = *N; + long int const rhs = *Rhs; + long int const lda = *Lda; + long int const ldb = *Ldb; + long int info = *Info; + dposv_(uplo, &n, &rhs, A, &lda, B, &ldb, &info); +} + +inline void dpotrf(const char *uplo, int const *N, double *A, int const *Lda, int const *Info) { + long int const n = *N; + long int const lda = *Lda; + long int info = *Info; + dpotrf_(uplo, &n, A, &lda, &info); +} + +#elif CALS_MATLAB + +#include #include "blas.h" #include "lapack.h" +#include "mex.h" -enum CBLAS_ORDER -{ - CblasColMajor = 0, CblasRowMajor = 1 -}; +#define CALS_BACKEND "MATLAB" -enum CBLAS_TRANSPOSE -{ - CblasTrans = 0, CblasNoTrans = 1 -}; +enum CBLAS_ORDER { CblasColMajor = 0, CblasRowMajor = 1 }; -enum CBLAS_DIAG -{ - CblasNonUnit = 0, CblasUnit = 1 -}; +enum CBLAS_TRANSPOSE { CblasTrans = 0, CblasNoTrans = 1 }; -enum CBLAS_UPLO -{ - CblasLower = 0, CblasUpper = 1 -}; +enum CBLAS_DIAG { CblasNonUnit = 0, CblasUnit = 1 }; -enum CBLAS_SIDE -{ - CblasRight = 0, CblasLeft = 1 -}; +enum CBLAS_UPLO { CblasLower = 0, CblasUpper = 1 }; -#define CALS_BACKEND "MATLAB" +enum CBLAS_SIDE { CblasRight = 0, CblasLeft = 1 }; -inline double cblas_dnrm2(long int n_elements, double *data, long int stride) -{ +inline double cblas_dnrm2(long int n_elements, double *data, long int stride) { return dnrm2_(&n_elements, data, &stride); } -inline double cblas_dasum(long int n_elements, double *data, long int stride) -{ +inline double cblas_dasum(long int n_elements, double *data, long int stride) { return dasum_(&n_elements, data, &stride); } -inline long int cblas_idamax(long int n_elements, double *data, long int stride) -{ +inline long int cblas_idamax(long int n_elements, double *data, long int stride) { return idamax_(&n_elements, data, &stride) - 1; } inline void -cblas_dcopy(long int n_elements, double const *src_data, long int src_stride, double *dst_data, long int dst_stride) -{ +cblas_dcopy(long int n_elements, double const *src_data, long int src_stride, double *dst_data, long int dst_stride) { dcopy_(&n_elements, src_data, &src_stride, dst_data, &dst_stride); } -inline void -cblas_dscal(long int n_elements, double scalar, double *data, long int stride) -{ +inline void cblas_dscal(long int n_elements, double scalar, double *data, long int stride) { dscal_(&n_elements, &scalar, data, &stride); } -inline void -cblas_daxpy(long int n_elements, double scalar, double *x, long int ldx, double *y, long int ldy) -{ +inline void cblas_daxpy(long int n_elements, double scalar, double *x, long int ldx, double *y, long int ldy) { daxpy_(&n_elements, &scalar, x, &ldx, y, &ldy); } -inline void -cblas_dgemm(CBLAS_ORDER Order, CBLAS_TRANSPOSE TransA, CBLAS_TRANSPOSE TransB, long int M, long int N, long int K, - double alpha, const double *A, long int lda, const double *B, long int ldb, double beta, double *C, - long int ldc) -{ +inline void cblas_dgemm(CBLAS_ORDER Order, + CBLAS_TRANSPOSE TransA, + CBLAS_TRANSPOSE TransB, + long int M, + long int N, + long int K, + double alpha, + const double *A, + long int lda, + const double *B, + long int ldb, + double beta, + double *C, + long int ldc) { assert(Order == CblasColMajor); const char *transa = TransA == CblasTrans ? "T" : "N"; const char *transb = TransB == CblasTrans ? "T" : "N"; dgemm_(transa, transb, &M, &N, &K, &alpha, A, &lda, B, &ldb, &beta, C, &ldc); } -inline void -cblas_dgemv(CBLAS_ORDER order, CBLAS_TRANSPOSE TransA, long int M, long int N, - double alpha, const double *A, long int lda, const double *X, long int incX, double beta, - double *Y, long int incY) -{ +inline void cblas_dgemv(CBLAS_ORDER order, + CBLAS_TRANSPOSE TransA, + long int M, + long int N, + double alpha, + const double *A, + long int lda, + const double *X, + long int incX, + double beta, + double *Y, + long int incY) { assert(order == CblasColMajor); const char *transa = TransA == CblasTrans ? "T" : "N"; dgemv(transa, &M, &N, &alpha, A, &lda, X, &incX, &beta, Y, &incY); } -inline void cblas_dtrsm(CBLAS_ORDER Order, CBLAS_SIDE Side, CBLAS_UPLO Uplo, CBLAS_TRANSPOSE TransA, - CBLAS_DIAG Diag, long int M, long int N, double alpha, const double *A, long int lda, - double *B, long int ldb) -{ +inline void cblas_dtrsm(CBLAS_ORDER Order, + CBLAS_SIDE Side, + CBLAS_UPLO Uplo, + CBLAS_TRANSPOSE TransA, + CBLAS_DIAG Diag, + long int M, + long int N, + double alpha, + const double *A, + long int lda, + double *B, + long int ldb) { assert(Order == CblasColMajor); const char *transa = TransA == CblasTrans ? "T" : "N"; const char *side = Side == CblasLeft ? "L" : "R"; @@ -148,8 +156,14 @@ inline void cblas_dtrsm(CBLAS_ORDER Order, CBLAS_SIDE Side, CBLAS_UPLO Uplo, CBL dtrsm_(side, uplo, transa, diag, &M, &N, &alpha, A, &lda, B, &ldb); } -inline void dposv(const char *uplo, int const *N, int const *Rhs, double *A, int const *Lda, double *B, int const *Ldb, int const *Info) -{ +inline void dposv(const char *uplo, + int const *N, + int const *Rhs, + double *A, + int const *Lda, + double *B, + int const *Ldb, + int const *Info) { long int const n = *N; long int const rhs = *Rhs; long int const lda = *Lda; @@ -158,8 +172,7 @@ inline void dposv(const char *uplo, int const *N, int const *Rhs, double *A, int dposv_(uplo, &n, &rhs, A, &lda, B, &ldb, &info); } -inline void dpotrf(const char *uplo, int const *N, double *A, int const *Lda, int const *Info) -{ +inline void dpotrf(const char *uplo, int const *N, double *A, int const *Lda, int const *Info) { long int const n = *N; long int const lda = *Lda; long int info = *Info; @@ -172,4 +185,4 @@ void set_threads(int threads); int get_threads(); -#endif //CALS_CALS_BLAS_H +#endif // CALS_CALS_BLAS_H diff --git a/include/cuda_utils.h b/include/cuda_utils.h index aa09277..ce5b7a1 100644 --- a/include/cuda_utils.h +++ b/include/cuda_utils.h @@ -1,40 +1,3 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #ifndef CALS_CUDA_UTILS_H #define CALS_CUDA_UTILS_H @@ -45,7 +8,6 @@ #include int const cuda_n_streams = 2; -static cudaStream_t custream{}; namespace cuda { @@ -57,12 +19,6 @@ namespace cuda void destroy_stream(cudaStream_t &stream); - inline void init_custream() - { initialize_stream(custream); }; - - inline void destroy_custream() - { destroy_stream(custream); }; - void allocate(double* &d_A, long int size); void deallocate(double* &d_A); @@ -99,13 +55,15 @@ namespace cuda CudaParams() { handle = cuda::cublas::create_handle(); - for (auto & stream : streams) initialize_stream(stream); + for (auto & stream : streams) + initialize_stream(stream); } ~CudaParams() { cuda::cublas::destroy_handle(handle); - for (auto & stream : streams) destroy_stream(stream); + for (auto & stream : streams) + destroy_stream(stream); } }; } diff --git a/include/definitions.h b/include/definitions.h index b197f6c..7b51fe5 100644 --- a/include/definitions.h +++ b/include/definitions.h @@ -1,40 +1,3 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #ifndef CALS_DEFINITIONS_H #define CALS_DEFINITIONS_H @@ -50,4 +13,6 @@ #define TIME(exp) ; #endif +typedef size_t dim_t; + #endif // CALS_DEFINITIONS_H diff --git a/include/experiments/bench_mttkrp.h b/include/experiments/bench_mttkrp.h new file mode 100644 index 0000000..6b3d70e --- /dev/null +++ b/include/experiments/bench_mttkrp.h @@ -0,0 +1,90 @@ +#ifndef CALS_BENCH_MTTKRP_H +#define CALS_BENCH_MTTKRP_H + +#include +#include +#include + +#include "tensor.h" +#include "matrix.h" +#include "timer.h" +#include "utils/utils.h" +#include "utils/mttkrp.h" + +#include "experiments/bench_mttkrp_ctf.h" +#include "experiments/bench_mttkrp_cals.h" +#include "experiments/bench_mttkrp_planc.h" + +namespace cals::bench +{ + void benchmark_mttkrp(const vector modes, int num_threads, int method, bool cuda_f=false) + { + set_threads(num_threads); + std::cout << "Threads: " << get_threads() << std::endl; + + //////////////////////////////////////////////////////////////////////////////////// + // Create CSV file name + //////////////////////////////////////////////////////////////////////////////////// + std::string folder = std::string(SOURCE_DIR) + "/data/" + std::string(CALS_BACKEND) + "/benchmark/"; + std::string is_cuda = (cuda_f) ? "CUDA_" : ""; + std::string modes_string = cals::utils::mode_string(modes); + std::string file_name; + file_name = folder + + "benchmark_" + + is_cuda + + std::string(CALS_BACKEND) + "_" + + modes_string + "_" + + std::to_string(get_threads()) + "_" + + std::to_string(method) + + ".csv"; + + initialize_csv(file_name); + std::cout << "File name: " << file_name << std::endl; + + //////////////////////////////////////////////////////////////////////////////////// + // Calculate target components + //////////////////////////////////////////////////////////////////////////////////// + vector components; + components.reserve(300); + for (auto i = 1; i < 20; i += 1) + components.push_back(i); + for (auto i = 20; i < 100; i += 10) + components.push_back(i); + for (auto i = 100; i < 1000; i += 100) + components.push_back(i); + for (auto i = 1000; i <= 5000; i += 1000) + components.push_back(i); + + int n_modes = static_cast(modes.size()); + for (auto m = 0; m < n_modes; m++) + { + for (auto comp : components) + { + if (method <= cals::mttkrp::MTTKRP_METHOD::AUTO) + { + auto params = cals::mttkrp::MttkrpParams(); + params.method = static_cast(method); + params.cuda = cuda_f; + params.lut = mttkrp::read_lookup_table(modes, get_threads(), params.cuda); + + auto result = benchmark_mttkrp_cals(comp, m, modes, cals::mttkrp::mttkrp, params); + append_csv(file_name, m, modes_string, comp, num_threads, result); + } else if (method == EXTERNAL_MTTKRP_METHODS::CTF) + { + auto result = benchmark_mttkrp_ctf(comp, m, modes); + append_csv(file_name, m, modes_string, comp, num_threads, result); + } else if (method == EXTERNAL_MTTKRP_METHODS::PLANC) + { + auto result = benchmark_mttkrp_planc(comp, m, modes); + append_csv(file_name, m, modes_string, comp, num_threads, result); + } else + { + std::cerr << "MTTKRP method " << method << " not found. Exiting." << std::endl; + exit(EXIT_FAILURE); + } + } + } + } +} + +#endif //CALS_BENCH_MTTKRP_H diff --git a/include/experiments/bench_mttkrp_cals.h b/include/experiments/bench_mttkrp_cals.h new file mode 100644 index 0000000..fac83f0 --- /dev/null +++ b/include/experiments/bench_mttkrp_cals.h @@ -0,0 +1,96 @@ +#ifndef CP_CALS_BENCH_MTTKRP_CALS_H +#define CP_CALS_BENCH_MTTKRP_CALS_H + +#include +#include +#include + +#include "tensor.h" +#include "matrix.h" +#include "timer.h" +#include "utils/utils.h" +#include "utils/mttkrp.h" + +#include "experiments/bench_utils.h" + +namespace cals::bench +{ + template + BenchResult benchmark_mttkrp_cals(int rank, int mode, vector modes, F &&f, cals::mttkrp::MttkrpParams ¶ms) + { + auto T = Tensor(modes); + auto cache = Matrix(2000, 2000); + auto ktensor = Ktensor(rank, modes); + + std::sort(modes.begin(), modes.end(), std::greater<>()); + vector workspace; + workspace.emplace_back(Matrix(modes[0] * modes[1], rank)); + + if (params.cuda) + { +#if CUDA_ENABLED + T.allocate_cudata(T.get_n_elements()); + + for (auto &f : ktensor.get_factors()) + f.allocate_cudata(f.get_n_elements()); + + for (auto &w : workspace) + w.allocate_cudata(w.get_n_elements()); +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + abort(); +#endif + } + + vector time; + time.reserve(ITERATIONS); + vector f_time; + f_time.reserve(ITERATIONS); + vector s_time; + s_time.reserve(ITERATIONS); + + for (auto i = 0; i < ITERATIONS; i++) + { + T.randomize(); + workspace[0].randomize(); + ktensor.randomize(); + + if (params.cuda) + { +#if CUDA_ENABLED + T.send_to_device(); + + for (auto &f : ktensor.get_factors()) + f.send_to_device(); + + for (auto &w : workspace) + w.send_to_device(); +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + abort(); +#endif + } + if (!params.cuda) + for (dim_t j = 0; j < cache.get_n_elements(); j++) + cache[j] += 0.0001; + + std::forward(f)(T, ktensor, workspace, mode, params); + time.push_back( + params.mttkrp_timers.timers[MttkrpTimers::TIMERS::MT_KRP].get_time() + + params.mttkrp_timers.timers[MttkrpTimers::TIMERS::MT_GEMM].get_time() + + params.mttkrp_timers.timers[MttkrpTimers::TIMERS::TS_GEMM].get_time() + + params.mttkrp_timers.timers[MttkrpTimers::TIMERS::TS_GEMV].get_time()); + f_time.push_back(params.mttkrp_timers.timers[MttkrpTimers::TIMERS::MT_KRP].get_time() + + params.mttkrp_timers.timers[MttkrpTimers::TIMERS::TS_GEMM].get_time()); + s_time.push_back(params.mttkrp_timers.timers[MttkrpTimers::TIMERS::MT_GEMM].get_time() + + params.mttkrp_timers.timers[MttkrpTimers::TIMERS::TS_GEMV].get_time()); + } + return {*std::min_element(time.begin(), time.end()), + *std::min_element(f_time.begin(), f_time.end()), + *std::min_element(s_time.begin(), s_time.end()), + params.flops, params.memops}; + } +} + + +#endif //CP_CALS_BENCH_MTTKRP_CALS_H diff --git a/include/experiments/bench_mttkrp_ctf.h b/include/experiments/bench_mttkrp_ctf.h new file mode 100644 index 0000000..3fa1e57 --- /dev/null +++ b/include/experiments/bench_mttkrp_ctf.h @@ -0,0 +1,77 @@ +#ifndef CP_CALS_BENCH_MTTKRP_CTF_H +#define CP_CALS_BENCH_MTTKRP_CTF_H + +#include +#include +#include + +#include "tensor.h" +#include "matrix.h" +#include "timer.h" +#include "utils/utils.h" +#include "utils/mttkrp.h" + +#include "experiments/bench_mttkrp.h" +#include "experiments/bench_utils.h" + +#if WITH_CTF +#include "mpi.h" +#include "ctf.hpp" +#endif + +namespace cals::bench +{ + BenchResult benchmark_mttkrp_ctf(int rank, int mode, vector modes) + { +#if WITH_CTF + // CTF Initialization + int lens[3] = {modes[0], modes[1], modes[2]}; + int lens_A[2] = {lens[0],rank}; + int lens_B[2] = {lens[1],rank}; + int lens_C[2] = {lens[2],rank}; + bool is_sparse = false; + + auto T = CTF::Tensor(3, is_sparse, lens); + + auto A = CTF::Matrix(lens_A[0], rank); + auto B = CTF::Matrix(lens_B[0], rank); + auto C = CTF::Matrix(lens_C[0], rank); + + + Matrix cache(2000, 2000); + cache.randomize(); + + vector time; + time.reserve(ITERATIONS); + + cals::Timer timer; + + for (auto i = 0; i < ITERATIONS; i++) + { + T.fill_random(-1, 1); + A.fill_random(-1, 1); + B.fill_random(-1, 1); + C.fill_random(-1, 1); + for (auto j = 0; j < cache.get_n_elements(); j++) + cache[j] += 0.0001; + + timer.start(); + if (mode == 0) + A["ir"] = T["ijk"]*B["jr"]*C["kr"]; + else if (mode == 1) + B["jr"] = T["ijk"]*A["ir"]*C["kr"]; + else if (mode == 2) + C["kr"] = T["ijk"]*A["ir"]*B["jr"]; + timer.stop(); + time.push_back(timer.get_time()); + } + + return {*std::min_element(time.begin(), time.end()), 0, 0, 0, 0}; +#endif + + return {0, 0, 0, 0, 0}; + } +} + + +#endif //CP_CALS_BENCH_MTTKRP_CTF_H diff --git a/include/experiments/bench_mttkrp_planc.h b/include/experiments/bench_mttkrp_planc.h new file mode 100644 index 0000000..93b0ebf --- /dev/null +++ b/include/experiments/bench_mttkrp_planc.h @@ -0,0 +1,104 @@ +#ifndef CP_CALS_BENCH_MTTKRP_PLANC_H +#define CP_CALS_BENCH_MTTKRP_PLANC_H + +#include +#include +#include + +#include "tensor.h" +#include "matrix.h" +#include "timer.h" +#include "utils/utils.h" +#include "utils/mttkrp.h" + +#include "experiments/bench_mttkrp.h" +#include "experiments/bench_utils.h" + +#if WITH_PLANC + +#include +#include "ddt.hpp" + +#endif + +namespace cals::bench +{ + BenchResult benchmark_mttkrp_planc(int rank, int mode, vector modes) + { +#if WITH_PLANC + arma::uvec dims; + dims.resize(modes.size()); + for (auto i = 0; i < dims.size(); i++) + dims[i] = static_cast(modes[i]); + + planc::Tensor T(dims); + T.rand(); + planc::NCPFactors ncp_factors(dims, rank, false); + ncp_factors.randu(100); + +// cals::Tensor Tc(modes, T.m_data.data()); +// T.print(); +// Tc.print("Mine"); +// cals::Ktensor ktensor(rank, modes); +// for (auto f = 0; f < dims.size(); f++) +// for (auto i = 0; i < dims[f]; i++) +// for (auto j = 0; j < rank; j++) +// ktensor.get_factor(f)(i,j) = ncp_factors.factor(f).at(i, j); +// for (auto f = 0; f < dims.size(); f++) +// ncp_factors.factor(f).print("Factor " + std::to_string(f)); +// for (auto &f : ktensor.get_factors()) +// f.print("Mine"); + +// std::vector workspace; +// workspace.emplace_back(Matrix(modes[0] * modes[1], rank)); +// cals::mttkrp::MttkrpParams params; +// params.method = cals::mttkrp::MTTKRP_METHOD::AUTO; +// auto &out_cals = cals::mttkrp::mttkrp(Tc, ktensor, workspace, tmode, params); + +// out.print(); +// out_cals.print(); +// exit(EXIT_SUCCESS); + + Matrix cache(2000, 2000); + cache.randomize(); + + cals::Matrix out(modes[mode], rank); + + vector time; + time.reserve(ITERATIONS); + + cals::Timer timer; + + int split; + if (mode == 0) + split = 0; // or 1 NOLINT(bugprone-branch-clone) + else if (mode == 1) + split = 0; // Only + else if (mode == 2) + split = 1; // Only + else + split = 0; + + auto ddt = DenseDimensionTree(T, ncp_factors, split); + + for (auto i = 0; i < ITERATIONS; i++) + { + double multittv_time, mttkrp_time; + + for (auto j = 0; j < cache.get_n_elements(); j++) + cache[j] += 0.0001; + + timer.start(); + ddt.in_order_reuse_MTTKRP(mode, out.get_data(), true, multittv_time, mttkrp_time); + timer.stop(); + time.push_back(timer.get_time()); + } + + return {*std::min_element(time.begin(), time.end()), 0, 0, 0, 0}; +#endif + + return {0, 0, 0, 0, 0}; + } +} + +#endif //CP_CALS_BENCH_MTTKRP_PLANC_H diff --git a/include/experiments/bench_utils.h b/include/experiments/bench_utils.h new file mode 100644 index 0000000..6e4a49c --- /dev/null +++ b/include/experiments/bench_utils.h @@ -0,0 +1,53 @@ +#ifndef CP_CALS_BENCH_UTILS_H +#define CP_CALS_BENCH_UTILS_H + +namespace cals::bench +{ + const static int ITERATIONS = 5; + + struct BenchResult + { + double time; + double first_time; + double second_time; + u_int64_t flops; + u_int64_t memops; + }; + + void initialize_csv(std::string &file_name, const std::string &sep = ";") + { + auto file = std::ofstream(file_name, std::ios::out); + file << "MODE" << sep + << "TENSOR_MODES" << sep + << "FLOPS" << sep + << "MEMOPS" << sep + << "RANK" << sep + << "THREADS" << sep + << "TIME" << sep + << "F_TIME" << sep + << "S_TIME" << std::endl; + } + + void append_csv(std::string &file_name, int mode, std::string &modes_string, int rank, int threads, + BenchResult &br, const std::string &sep = ";") + { + auto file = std::ofstream(file_name, std::ios::app); + file << mode << sep + << modes_string << sep + << br.flops << sep + << br.memops << sep + << rank << sep + << threads << sep + << br.time << sep + << br.first_time << sep + << br.second_time << std::endl; + } + + enum EXTERNAL_MTTKRP_METHODS + { + CTF = cals::mttkrp::MTTKRP_METHOD::AUTO + 1, + PLANC + }; +} + +#endif //CP_CALS_BENCH_UTILS_H diff --git a/include/experiments/experiments_utils.h b/include/experiments/experiments_utils.h index 3739bb0..fb366c7 100644 --- a/include/experiments/experiments_utils.h +++ b/include/experiments/experiments_utils.h @@ -1,45 +1,6 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #ifndef CALS_EXPERIMENTS_UTILS_H #define CALS_EXPERIMENTS_UTILS_H -#include -#include #include #include "cals.h" @@ -48,18 +9,20 @@ using std::vector; -void -compare_als_cals(int tensor_rank, const vector &modes, vector &ranks, int num_threads, cals::CalsParams ¶ms, - const std::basic_string& file_suffix = ""); - -void -compare_als_cals(const cals::Tensor &X, vector &ranks, int num_threads, cals::CalsParams ¶ms, - const std::basic_string& file_suffix = ""); - -void run_cals(int tensor_rank, const vector &modes, vector &ranks, int num_threads, cals::CalsParams ¶ms, - bool print_header, const std::basic_string& file_suffix = ""); - -void run_cals(const cals::Tensor &X, vector &ranks, int num_threads, cals::CalsParams ¶ms, - bool print_header, const std::basic_string& file_suffix = ""); +vector generate_components(int min, int max, int copies); + +void compare_als_cals(const cals::Tensor &X, + vector &components, + unsigned int num_threads, + cals::CalsParams ¶ms, + const std::basic_string &file_suffix = "", + bool reproducible = false); + +void run_cals(const cals::Tensor &X, + vector &components, + unsigned int num_threads, + cals::CalsParams ¶ms, + bool print_header, + const std::basic_string &file_suffix = ""); #endif //CALS_EXPERIMENTS_UTILS_H diff --git a/include/experiments/mttkrp_benchmarker.h b/include/experiments/mttkrp_benchmarker.h deleted file mode 100644 index 5dd51bd..0000000 --- a/include/experiments/mttkrp_benchmarker.h +++ /dev/null @@ -1,87 +0,0 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - -#ifndef CALS_MTTKRP_BENCHMARKER_H -#define CALS_MTTKRP_BENCHMARKER_H - -#include -#include - -#include "tensor.h" -#include "matrix.h" -#include "timer.h" -#include "utils.h" - -const int iterations = 10; - -namespace cals::benchmarker -{ - void benchmark_mttkrp(const std::vector &modes, int num_threads, cals::ops::MttkrpParams &mttkrp_params); - - template - double benchmark(int rank, int mode, Tensor &T, F &&f, cals::ops::MttkrpParams ¶ms) - { - // Initialize operands - Matrix cache(2000, 2000); - - auto modes = T.get_modes(); - cals::Ktensor ktensor(rank, modes); - - std::sort(modes.begin(), modes.end(), std::greater<>()); - - vector workspace; - workspace.emplace_back(Matrix(modes[0] * modes[1], rank)); - - vector time; - time.reserve(iterations); - - for (auto i = 0; i < iterations; i++) - { - T.randomize(); - workspace[0].randomize(); - ktensor.randomize(); - for(auto j = 0; j < cache.get_n_elements(); j++)cache[j] += 0.0001; - - std::forward(f)(T, ktensor, workspace, mode, params); - time.push_back(params.timer_mttkrp_gemm->get_time() + params.timer_mttkrp_krp->get_time() - + params.timer_twostep_gemm->get_time() + params.timer_twostep_gemv->get_time()); - } - return *std::min_element(time.begin(), time.end()); - } -} - -#endif //CALS_MTTKRP_BENCHMARKER_H diff --git a/include/ktensor.h b/include/ktensor.h index ccc364b..1e04eaa 100644 --- a/include/ktensor.h +++ b/include/ktensor.h @@ -1,238 +1,246 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #ifndef CALS_KTENSOR_H #define CALS_KTENSOR_H -#include #include +#include +#include -#include "matrix.h" #include "cals_blas.h" +#include "matrix.h" -using std::vector; using std::multiplies; +using std::vector; static int universal_ktensor_id = 1; -namespace cals -{ - class Ktensor - { - int id{-1}; - int rank{-1}; - int iters{-1}; - - double approx_error{0}; - double approx_error_diff{0}; - - double fit{0}; - double fit_diff{0}; - - bool normalized{false}; - - vector>> active_set; - - vector lambda{}; - vector factors{}; - - void rec_to_tensor(vector &modes, int &index, double *x_data, int level, int *dim_ind); - - public: - Ktensor() = default; - - /** Allocates a Ktensor (no initialization). - * - * This constructor allocates a Ktensor of a specific \p rank and sizes of \p modes. The - * contents of the Ktensor created are not initialized. One can use the randomize member function - * to fill in the Ktensor with random values. - * - * @param rank The rank of the Ktensor. - * @param vector A vector containing the sizes of each mode of the Ktensor. - */ - Ktensor(const int rank, const vector &modes) - : id{universal_ktensor_id++}, - rank{rank}, - active_set{vector>>(modes.size(), vector>())}, - factors{vector(modes.size())} - { - for (auto n = 0lu; n < modes.size(); n++) - { - factors[n] = Matrix{modes[n], rank}; - active_set[n] = vector>(modes[n], vector(rank, true)); - } - - // For each mode allocate a coefficients vector - lambda.resize(get_rank()); - } - - Ktensor &operator=(Ktensor &&rhs) = default; - - Ktensor(Ktensor &&rhs) = default; - - Ktensor(const Ktensor &rhs) - : id{universal_ktensor_id++}, - rank{rhs.rank}, - active_set{vector>>(rhs.get_n_modes(), vector>())}, - lambda{vector(rhs.rank)}, - factors{vector(rhs.get_n_modes())} - { - for (auto n = 0; n < rhs.get_n_modes(); n++) - { - factors[n] = rhs.get_factor(n); - active_set[n] = vector>(get_factor(n).get_rows(), vector(rank, true)); - } - lambda = rhs.get_lambda(); +namespace cals { +class Ktensor { + int id{-1}; /*< Unique ID of the Ktensor */ + int components{-1}; /*< Number of components of the Ktensor */ + int iters{-1}; /*< Number of iterations spent in ALS */ + + double fit{0.0}; /*< The fit to the target tensor */ + double old_fit{0.0}; /*< The fit in the previous iteration */ + double approx_error{0.0}; /*< The approximation error of the Ktensor to the target Tensor */ + + bool normalized{false}; /*< Boolean indicating whether the Ktensor is normalized or not */ + + vector>> active_set; /*< Active set for each factor matrix of last iteration (Used in NNLS) */ + + vector lambda{}; /*< Vector containing the lambda normalization factors per column of the factor matrices */ + vector factors{}; /*< Vector containing the factor matrices of the Ktensor */ + + /** Recursive function that is used to compute the reconstruction of the Ktensor into a Tensor. + */ + void rec_to_tensor(vector &modes, int &index, double *x_data, int level, dim_t *dim_ind); + +public: + Ktensor() = default; + + /** Allocates a Ktensor (no initialization). + * + * This constructor allocates a Ktensor of a specific \p rank and sizes of \p modes. The + * contents of the Ktensor created are not initialized. One can use the randomize member function + * to fill in the Ktensor with random values. + * + * @param rank The rank of the Ktensor. + * @param vector A vector containing the sizes of each mode of the Ktensor. + */ + Ktensor(int rank, const vector &modes) + : id{universal_ktensor_id++}, components{rank}, + active_set{vector>>(modes.size(), vector>())}, factors{vector( + modes.size())} { + assert(rank > 0); + for (dim_t n = 0; n < modes.size(); n++) { + factors[n] = Matrix{modes[n], static_cast(rank)}; + active_set[n] = vector>(modes[n], vector(rank, true)); } - Ktensor &operator=(const Ktensor &rhs) - { - if (this == &rhs) // Properly handle self assignment - return *this; - - id = rhs.id; - rank = rhs.rank; - lambda = rhs.get_lambda(); - - factors = vector(rhs.get_n_modes()); - for (auto n = 0; n < rhs.get_n_modes(); n++) factors[n] = rhs.get_factor(n); - return *this; - } - - ~Ktensor() = default; - - // TODO clean this. Rank should either not exist or be updated properly when adjusting the dimensions of a Ktensor. - inline int get_rank() const noexcept - { return factors[0].get_cols(); } - - inline int get_iters() const noexcept - { return iters; } - - inline void set_iters(int new_iters) noexcept - { iters = new_iters; } - - inline int get_id() const noexcept - { return id; } - - inline double get_approx_error_diff() const noexcept - { return approx_error_diff; } - - inline void set_approx_error_diff(double new_error_diff) noexcept - { approx_error_diff = new_error_diff; } - - inline double get_approx_error() const noexcept - { return approx_error; } + // For each mode allocate a coefficients vector + lambda.resize(get_components()); + } - inline void set_approx_error(double new_error) noexcept - { approx_error = new_error; } + Ktensor &operator=(Ktensor &&rhs) = default; - inline double get_fit_diff() const noexcept - { return fit_diff; } + Ktensor(Ktensor &&rhs) = default; - inline void set_fit_diff(double new_fit_diff) noexcept - { fit_diff = new_fit_diff; } - - inline double get_fit() const noexcept - { return fit; } - - inline void set_fit(double new_fit) noexcept - { fit = new_fit; } - - inline int get_n_modes() const noexcept - { return factors.size(); } - - inline Matrix const &get_last_factor() const noexcept - { return factors.back(); } - - inline Matrix const &get_factor(const int mode) const noexcept - { return factors.at(mode); } - - inline vector> &get_active_set(const int mode) noexcept - { return active_set.at(mode); } - - inline Matrix &get_factor(const int mode) noexcept - { return factors.at(mode); } - - inline vector const &get_factors() const noexcept - { return factors; } - - inline vector &get_factors() noexcept - { return factors; } - - inline vector const &get_lambda() const noexcept - { return lambda; } - - inline vector &get_lambda() noexcept - { return lambda; } - - inline void set_factor(int index, double* data) noexcept - { - auto &target = get_factor(index); - cblas_dcopy(target.get_n_elements(), data, 1, target.get_data(), 1); - } - - inline void set_lambda(double const * data) noexcept - { - auto i = 0; - for (auto &l : lambda) l = data[i++]; + Ktensor(const Ktensor &rhs) + : id{universal_ktensor_id++}, components{rhs.components}, active_set{vector>>( + rhs.get_n_modes(), vector>())}, + lambda{vector(static_cast(rhs.components))}, factors{vector(rhs.get_n_modes())} { + for (dim_t n = 0; n < rhs.get_n_modes(); n++) { + factors[n] = rhs.get_factor(n); + active_set[n] = vector>(get_factor(n).get_rows(), vector(components, true)); } + lambda = rhs.get_lambda(); + } - void print(const std::basic_string& text = "Ktensor") const; - - Ktensor &attach(vector &data_ptrs, bool multi_thread = true); - - Ktensor &detach(); - - Ktensor &normalize(); - - Ktensor &normalize(int mode, int iteration = 1); - - Ktensor &denormalize(); + Ktensor &operator=(const Ktensor &rhs) { + if (this == &rhs) // Properly handle self assignment + return *this; - Ktensor &randomize(); + id = rhs.id; + components = rhs.components; + lambda = rhs.get_lambda(); + + factors = vector(rhs.get_n_modes()); + for (dim_t n = 0; n < rhs.get_n_modes(); n++) + factors[n] = rhs.get_factor(n); + return *this; + } - Tensor to_tensor(); + ~Ktensor() = default; + + // Getters + // TODO clean this. Components should either not exist or be updated properly when adjusting the dimensions of a + // Ktensor. + [[nodiscard]] inline int get_components() const noexcept { return factors[0].get_cols(); } + + [[nodiscard]] inline int get_iters() const noexcept { return iters; } + + [[nodiscard]] inline int get_id() const noexcept { return id; } + + inline double get_approximation_error() const noexcept { return approx_error; } + + inline vector &get_factors() noexcept { return factors; } + + inline vector const &get_lambda() const noexcept { return lambda; } + + inline vector &get_lambda() noexcept { return lambda; } + + // Setters + inline void set_iters(int new_iters) noexcept { iters = new_iters; } + + inline void set_approximation_error(double new_error) noexcept { approx_error = new_error; } + + inline void set_factor(int index, double *data) noexcept { + auto &target = get_factor(static_cast(index)); + cblas_dcopy(target.get_n_elements(), data, 1, target.get_data(), 1); + } + + inline void set_lambda(double const *data) noexcept { + auto i = 0; + for (auto &l : lambda) + l = data[i++]; + } + + /** Calculate the fit to the target Tensor. + * + * @param X_norm L2-Norm of the target Tensor. + * + * @return The fit to the target Tensor. + */ + inline double calculate_new_fit(double X_norm) noexcept { + assert(X_norm != 0); + old_fit = fit; + fit = 1 - std::fabs(approx_error) / X_norm; + return fit; + } + + /** Calculate the difference in fit between the last iterations. + * + * @return The difference in fit. + */ + [[nodiscard]] inline double get_fit_diff() const noexcept { return std::fabs(old_fit - fit); } + + /** Get the number of modes of the Ktensor. + */ + inline dim_t get_n_modes() const noexcept { return static_cast(factors.size()); } + + /** Get a reference to the last factor Matrix. + */ + inline Matrix const &get_last_factor() const noexcept { return factors.back(); } + + /** Get a reference to a specific factor Matrix. + * + * @param mode The mode for which to get the factor Matrix. + */ + inline Matrix const &get_factor(dim_t mode) const noexcept { return factors.at(mode); } + + inline Matrix &get_factor(dim_t mode) noexcept { return factors.at(mode); } + + inline vector> &get_active_set(const int mode) noexcept { return active_set.at(mode); } + + [[nodiscard]] inline vector const &get_factors() const noexcept { return factors; } + + /** Print the contents of the Ktensor, together with some optional text. + * + * @param text (Optional) Text to display along with the contents of the Ktensor. + */ + void print(const std::string &&text = "Ktensor") const; + + /** Attach every factor matrix to pointers (and copy their contents to the new locations). + * + * @param data_ptrs vector containing the pointers to which the factor matrices should point (must be the same length + * as components) + * @param multi_thread (Optional) Whether it should be performed using multiple threads or not (to avoid parallel copy + * of overlaping memory regions). + * + * @return Reference to self. + */ + Ktensor &attach(vector &data_ptrs, bool multi_thread = true); + + /** Reset the factor matrices to point to the data they own. + */ + Ktensor &detach(); + + /** Normalize the Ktensor. + */ + Ktensor &normalize(); + + /** Normalize a specific factor matrix. + * + * @param mode Specify the factor matrix to normalize. + * @param iteration Depending on the iteration a different normalization function is used. + * + * @return Reference to self. + */ + Ktensor &normalize(int mode, int iteration = 1); + + /** Remove normalization of the Ktensor. + * + * @return Reference to self. + */ + Ktensor &denormalize(); + + /** Randomize the Ktensor. + * + * @param r (Optional) Whether to use the global pre-seeded generator to create a reproducible set of values for + * testing/experiments. + * + * @return Reference to self. + */ + Ktensor &randomize(); + + /** Fill the Ktensor with values. + * + * @param f function that returns a double every time it is invoked. + * + * @return Reference to self. + */ + Ktensor &fill(function &&func); + + /** Convert the Ktensor to a Tensor. + * + * @return The reconstructed tensor. + */ + Tensor to_tensor(); #if CUDA_ENABLED - Ktensor &cuattach(vector &cudata_ptrs); - - Ktensor &cudetach(); + /** Attach every factor matrix to pointers (for GPU) (and copy their contents to the new locations). + * + * @param data_ptrs vector containing the pointers to which the factor matrices should point (must be the same length + * as components) + * + * @return Reference to self. + */ + Ktensor &cuattach(vector &cudata_ptrs, cudaStream_t &stream); + + /** Reset the factor matrices to point to the data they own. + */ + Ktensor &cudetach(); #endif - }; +}; } // namespace cals #endif // CALS_KTENSOR_H diff --git a/include/matrix.h b/include/matrix.h index dd32745..0c0a151 100644 --- a/include/matrix.h +++ b/include/matrix.h @@ -1,138 +1,155 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #ifndef CALS_MATRIX_H #define CALS_MATRIX_H #include "tensor.h" -namespace cals -{ - class Matrix : public Tensor - { - int rows{}; - int cols{}; - int row_stride{}; - int col_stride{}; - - public: - // Constructors and Destructor - Matrix() = default; - - ~Matrix() = default; - - Matrix(int dim0, int dim1); - - Matrix(int dim0, int dim1, double *view_data); - - Matrix(int dim0, int dim1, int row_stride, int col_stride, double *view_data = nullptr); - - // Move and Copy Constructors - Matrix(Matrix &&rhs) = default; - - Matrix &operator=(Matrix &&rhs) = default; - - Matrix(const Matrix &rhs) = default; - - Matrix &operator=(const Matrix &rhs) = default; - - // Getters - inline int get_rows() const noexcept - { return rows; }; - - inline int get_cols() const noexcept - { return cols; }; - - inline int get_row_stride() const noexcept - { return row_stride; }; - - inline int get_col_stride() const noexcept - { return col_stride; }; - - inline double &at(int row, int col) - { return get_data()[row * row_stride + col * col_stride]; }; - - inline double at(int row, int col) const noexcept - { return get_data()[row * row_stride + col * col_stride]; }; - - Matrix &resize(int new_rows, int new_cols) noexcept - { - vector modes = {new_rows, new_cols}; - Tensor::resize(new_rows * new_cols, modes); - rows = new_rows; - cols = new_cols; - col_stride = new_rows; -// row_stride = 1; - return *this; - }; - - Matrix &hadamard(const Matrix &mat); - - Matrix &read(const Matrix &m); - - inline void attach(double *data) - { set_data(data); } - - inline void detach() - { reset_data(); } +namespace cals { +class Matrix : public Tensor { + dim_t rows{}; /*< Number of rows of the Matrix */ + dim_t cols{}; /*< Number of columns of the Matrix */ + dim_t col_stride{}; /*< Column stride of the Matrix (all matrices stored in col major order) */ + +public: + Matrix() = default; + + ~Matrix() = default; + + /** Generic constructor that allocates a Matrix (no initialization). + * + * This constructor allocates a Matrix with \p dim0 rows and \p dim 1 cols. + * The contents of the Matrix are not initialized. One can use the randomize member function + * to fill in the Matrix with random values. + * + * @param dim0 Desired number of rows. + * @param dim1 Desired number of cols. + */ + Matrix(dim_t dim0, dim_t dim1); + + /** Generic constructor that allocates a Matrix (no initialization). + * + * This constructor allocates a Matrix with \p dim0 rows and \p dim1 cols. + * The contents of the Matrix are not initialized. One can use the randomize member function + * to fill in the Matrix with random values. + * + * @param dim0 Desired number of rows. + * @param dim1 Desired number of cols. + * @param view_data (Optional) If set, the created Matrix object does not own data (RAII). Instead it points to + * already existing data. In this case, the Matrix is a "view". + */ + Matrix(dim_t dim0, dim_t dim1, double *view_data); + + // Move and Copy Constructors + Matrix(Matrix &&rhs) = default; + + Matrix &operator=(Matrix &&rhs) = default; + + Matrix(const Matrix &rhs) = default; + + Matrix &operator=(const Matrix &rhs) = default; + + // Getters + [[nodiscard]] inline dim_t get_rows() const noexcept { return rows; }; + + [[nodiscard]] inline dim_t get_cols() const noexcept { return cols; }; + + [[nodiscard]] inline dim_t get_col_stride() const noexcept { return col_stride; }; + + /** Operator to access an element in the Matrix, giving its row and column. (all matrices are stored in col-major) + * + * @param row row index of the element. + * @param col column index of the element. + * + * @return The element of the matrix + */ + inline double &operator()(dim_t row, dim_t col) { return get_data()[row + col * col_stride]; }; + + inline double operator()(dim_t row, dim_t col) const noexcept { return get_data()[row + col * col_stride]; }; + + /** "Soft" resize, meaning the dimensions of the Matrix are only changed (not the underlying memory) + * + * The new requested sizes must fit inside the memory initially allocated for the Matrix (max_n_elements). + * + * @param new_rows new number of rows. + * @param new_cols new number of columns. + * + * @return reference to self. + */ + Matrix &resize(dim_t new_rows, dim_t new_cols) noexcept { + vector modes = {new_rows, new_cols}; + Tensor::resize(new_rows * new_cols, modes); + rows = new_rows; + cols = new_cols; + col_stride = new_rows; + return *this; + }; - void print(const std::basic_string& text = "Matrix") const; + /** Compute the hadamard product between the Matrix itself and another Matrix. + * + * The matrices must be of the same size. + * + * @param mat Matrix with which to compute the hadamard product. + * + * @return reference to self. + */ + Matrix &hadamard(const Matrix &mat); + + /** Change the data member variable to point to a specific place in memory (other than the owned memory, if it + * exists). + * + * @param data pointer to memory where the Matrix should point to. + */ + inline void attach(double *data) { set_data(data); } + + /** Reset the data member variable to the memory "owned" by the Matrix (pointed to by the data_up variable). + */ + inline void detach() { reset_data(); } + + /** Print the contents of the Matrix, together with some optional text. + * + * @param text (Optional) Text to display along with the contents of the Matrix. + */ + void print(const std::string &&text = "Matrix") const; + + /** Print the size and other info regarding the Matrix. + */ + void info() const; + + /** Compute the one-norm per column of the Matrix and return the max value. + * + * @return maximum value among the sum of magnitudes of elements per column of the Matrix. + */ + [[nodiscard]] double one_norm() const { + auto max = -DBL_MAX; + for (dim_t col = 0; col < get_cols(); col++) { + auto one_norm = cblas_dasum(get_rows(), get_data() + col * get_col_stride(), 1); + if (one_norm > max) + max = one_norm; + } + return max; + }; - void info() const; + /** Read a transposed version of a Matrix. + * + * @param rhs Matrix, of which the transposed version to read. + * + * @return Reference to self. + */ + Matrix &transpose_copy(const Matrix &rhs); - double one_norm() const - { - auto max = -DBL_MAX; - for (auto col = 0; col < get_cols(); col++) - { - auto one_norm = cblas_dasum(get_rows(), get_data() + col * get_col_stride(), 1); - if (one_norm > max) max = one_norm; - } - return max; - }; #if CUDA_ENABLED - inline void cuattach(double *cudata) - { set_cudata(cudata); } - - inline void cudetach() - { reset_cudata(); } + /** Change the data member variable (of the GPU) to point to a specific place in memory (other than the owned memory, + * if it exists) + * + * @param cudata pointer to memory where the Matrix should point to. + */ + inline void cuattach(double *cudata) { set_cudata(cudata); } + + /** Reset the data member variable (of the GPU) to the memory "owned" by the Matrix (pointed to by the data_up + * variable) + */ + inline void cudetach() { reset_cudata(); } #endif - - }; +}; } // namespace cals #endif // CALS_MATRIX_H diff --git a/include/multi_ktensor.h b/include/multi_ktensor.h index 44ce937..4664011 100644 --- a/include/multi_ktensor.h +++ b/include/multi_ktensor.h @@ -1,112 +1,119 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #ifndef CALS_MULTI_KTENSOR_H #define CALS_MULTI_KTENSOR_H -#include -#include "definitions.h" #include "ktensor.h" +#include -namespace cals -{ - struct RegistryEntry - { - Ktensor &ktensor; - vector gramians; - int col; - Ktensor ls_ktensor{}; - }; - - typedef std::map Registry; - - class MultiKtensor : public Ktensor - { - int occupancy{0}; // Number of occupied cols. - int start{0}; - int end{0}; - bool cuda{false}; - bool line_search{false}; - vector modes; - vector occupancy_vec; // Occupancy of the available buffer. - Registry registry; // Keeps track of Ktensors currently in the buffer. - - int check_availability(Ktensor &ktensor); - - MultiKtensor &adjust_edges(); - - public: - MultiKtensor() = default; - - ~MultiKtensor() = default; - - MultiKtensor &operator=(MultiKtensor &&mk) = default; - - explicit MultiKtensor(vector &modes, int buffer_size); - - MultiKtensor &add(Ktensor &ktensor); - - MultiKtensor &remove(int ktensor_id); - - Registry &get_registry() - { return registry; } - - inline int get_start() const noexcept - { return start; } - - MultiKtensor &compress(); - - inline void set_cuda(bool value) - { cuda = value; }; - - inline void set_line_search(bool value) - { line_search = value; }; - - inline vector &get_modes() - { return modes; }; - }; - - struct BufferFull : public std::exception - { - const char *what() const noexcept override - { - return "Buffer is full, wait until some ktensors converge."; - } +namespace cals { +/** Structure that defines all the relative information and workspaces to a model that has been entered to the + * multi-Ktensor (buffer). + * */ +struct RegistryEntry { + Ktensor &ktensor; /*< Reference to the Ktensor. */ + vector gramians; /*< Gramians associated to the Ktensor. */ + int col; /*< The starting column in each factor matrix, where the Ktensor was placed. */ + Ktensor ls_ktensor{}; /*< Workspace related to line search (only allocated if line search is enabled). */ + Ktensor ls_tr_ktensor{}; /*< Workspace related to line search (only allocated if line search is enabled). */ +}; + +/* A map associating every model currently in the buffer to data related to it */ +typedef std::map Registry; + +class MultiKtensor : public Ktensor { + int occupancy{0}; /*< Number of occupied cols.*/ + int start{0}; /*< starting column for every factor multi-matrix */ + int end{0}; /*< last column for every factor multi-matrix */ + vector occupancy_vec; /*< Vector indicating the id of each ktensor (model) occupying every column */ + + vector modes; /*< modes of the Ktensor (same as modes of the target tensor) */ + Registry registry; /*< Keeps track of Ktensors currently in the buffer. */ + + bool cuda{false}; /*< Whether CUDA is used (to also allocate GPU related memory) */ + bool line_search{false}; /*< Whether line search is used (to create workspace objects when inserting a new model) */ + + /** Check whether a specific Ktensor fits into the buffer + * + * @throws BufferFull If the Ktensor does not fit + * @return the index of the starting column, where the model can be placed in the buffer. + * */ + int check_availability(Ktensor &ktensor); + + /** Adjust the \p end member variable to point to the end of the last Ktensor in the buffer. + * */ + MultiKtensor &adjust_edges(); + +#if CUDA_ENABLED + cudaStream_t stream; +#endif + +public: + MultiKtensor() = default; + + ~MultiKtensor() = default; + + MultiKtensor &operator=(MultiKtensor &&mk) = default; + + /** Create an empty MultiKtensor with a fixed maximum buffer_size (number of columns per factor matrix) + * + * @param modes The modes of the target Tensor. + * @param buffer_size The maximum number of columns per factor matrix. Sets the upper limit of the sum of components + * of the models that can concurrently reside on the buffer. + * */ + explicit MultiKtensor(vector &modes, dim_t buffer_size); + + /** Add a Ktensor to the MultiKtensor. + * + * @throws BufferFull If the Ktensor does not fit. + * @returns Reference to self. + * */ + MultiKtensor &add(Ktensor &ktensor); + + /** Remove a Ktensor from the MultiKtensor. + * + * @param ktensor_id The unique global ID of the Ktensor. + * @returns Reference to self. + * */ + MultiKtensor &remove(int ktensor_id); + + // Getters & Setters + Registry &get_registry() { return registry; } + + [[nodiscard]] inline int get_start() const noexcept { return start; } + + inline void set_cuda(bool value) { cuda = value; }; + + inline void set_line_search(bool value) { line_search = value; }; + + [[maybe_unused]] inline vector &get_modes() { return modes; }; + + /** Return the id of the leftmost Ktensor in the buffer (usefull for experiments) + * + * */ + inline int get_leftmost_id() { + if (!occupancy_vec.empty()) + return occupancy_vec[0]; + else + return -1; }; -} -#endif //CALS_MULTI_KTENSOR_H + /** Compress the buffers to remove fragmentation. + * + * As models converge and get evicted from the \p occupancy_vec, fragmentation occurs in the factor matrices. + * This function traverses the \p occupancy_vec, detects values set to 0 (meaning no model resides in the particular + * column of every factor matrix). Then, it creates a set of operations that shift all the models to the start of + * each factor matrix (buffer). Finally, the \p end is adjusted using the corresponding member function. + * @returns Reference to self. + * */ + MultiKtensor &compress(); +}; + +/** Exception thrown when the buffer is full. + * */ +struct BufferFull : public std::exception { + [[nodiscard]] const char *what() const noexcept override { + return "Buffer is full, wait until some ktensors converge."; + } +}; +} // namespace cals + +#endif // CALS_MULTI_KTENSOR_H diff --git a/include/tensor.h b/include/tensor.h index 09e638a..97ac058 100644 --- a/include/tensor.h +++ b/include/tensor.h @@ -1,302 +1,313 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #ifndef CALS_TENSOR_H #define CALS_TENSOR_H #include +#include #include #include -#include +#include +#include +#include -#include "definitions.h" #include "cals_blas.h" +#include "definitions.h" #if CUDA_ENABLED + #include "cuda_utils.h" + #endif -using std::vector; -using std::unique_ptr; using std::function; +using std::unique_ptr; +using std::vector; -namespace cals -{ -#if CUDA_ENABLED - struct DDev { - void operator() (double* &dev_ptr) { - cuda::deallocate(dev_ptr); - } - }; -#endif - struct Dopnew { - void operator() (double* &host_ptr) { - operator delete(host_ptr); - } - }; - - struct Unfolding - { - int n_blocks; // The number of matrix blocks. - int block_offset; // The offset (# entries) between blocks in the tensor. - int rows; // The rows of one block. - int cols; // The cols of one block. - int stride; // The (row- or column-) stride of each block. - }; +namespace cals { - // TODO Create separate class for "data_up" to support an iterator, if it does not inhibit performance. - // or implement iterator for the tensor class to simplify certain C-like loops - class Tensor - { - int n_elements{}; - int max_n_elements{}; - vector modes; +/* variable specifying the alignment with which to allocate performance critical memory segments */ +constexpr std::align_val_t alignment = static_cast(64); #if CUDA_ENABLED - mutable unique_ptr cudata_up{nullptr, DDev()}; - mutable double *cudata{}; -#endif - - unique_ptr data_up{nullptr, Dopnew()}; - double *data{}; - - int rank{0}; - - public: - // Constructors and Destructor - Tensor() = default; - - ~Tensor() = default; - - /** Generic constructor that allocates a Tensor (no initialization). - * - * This constructor allocates a Tensor with specific sizes of \p modes. - * The contents of the Tensor created are not initialized. One can use the randomize member function - * to fill in the Tensor with random values. - * - * @param modes A vector containing the sizes of each mode of the Ktensor. - */ - explicit Tensor(const vector &modes); - - /** Constructor that uses pre-existing Tensor. - * - * This constructor creates a Tensor object which points to data (does not own it) already existing in memory. - * It is meant to be used, for example, with Matlab, to view a Tensor already initialized in Matlab. - * - * @param modes A vector containing the sizes of each mode of the Ktensor. - * @param view_data Pointer to the location of the Tensor data. - */ - explicit Tensor(const vector &modes, double *view_data); - - /** Constructor that reads a Tensor from a file. - * - * This constructor creates a Tensor, whose contents are read from a file. - * The first line of the file must contain the dimensions, separated by spaces. - * The rest of the file should contain the elements of the Tensor, separated by the newline character. - * - * @param file_name The name of the file to read the Tensor from. - */ - explicit Tensor(const std::string &file_name); - - /** Constructor that simplifies the creation of Matrices (2D tensors). - * - * @param mode0 Number of rows of a Matrix. - * @param mode1 Number of columns of a Matrix. - * @param view_data (Optional) If set, the Matrix created points to data already existing in memory (does not - * own that data). Otherwise, it allocates an (un-initialized) Matrix. - */ - Tensor(int mode0, int mode1, double *view_data = nullptr); // Constructor to simplify creation of matrices - - /** Creates a Tensor of a specific rank. - * - * This constructor creates a Tensor of a specific rank by first creating a randomly initialized Ktensor - * of a specific \p rank, and then transforms that Ktensor into a full Tensor. - * - * @param rank The desired rank of the Tensor. - * @param modes A vector containing the sizes of each mode of the Ktensor. - */ - Tensor(int rank, const vector &modes); - - // Move and Copy Constructors - Tensor(Tensor &&rhs) = default; - - Tensor &operator=(Tensor &&rhs) = default; - - Tensor(const Tensor &); - - Tensor &operator=(const Tensor &rhs); - - // Getters - inline int get_n_elements() const noexcept - { return n_elements; }; - - inline int get_max_n_elements() const noexcept - { return max_n_elements; }; - - inline int get_mode(const int mode) const noexcept - { return modes[mode]; }; - inline int get_n_modes() const noexcept - { return modes.size(); }; +static void Ddev(double *dev_ptr) { cuda::deallocate(dev_ptr); } - inline vector get_modes() const noexcept - { return modes; }; - - inline double *get_data() const noexcept - { return data; }; - - inline void set_data(double *new_data) noexcept - { data = new_data; }; - - inline Tensor &reset_data() noexcept - { - data = data_up.get(); - return *this; - }; - - inline int get_rank() const noexcept - { return rank; }; - - inline bool is_view() const noexcept - { return data_up == nullptr; }; - - inline void reset() noexcept - { - data_up.reset(); - data = nullptr; - } - - inline double const &operator[](int index) const noexcept - { return data[index]; } - - inline double &operator[](int index) noexcept - { return data[index]; } - - void resize(int new_n_elements, vector &new_modes) - { - assert(new_n_elements <= max_n_elements); - assert(new_modes.size() == modes.size()); - - n_elements = new_n_elements; - modes = std::move(new_modes); - } - - double norm() const - { return cblas_dnrm2(n_elements, data, 1); }; - - Tensor &fill(const function &&f); - - Tensor &zero(); - - Tensor &randomize(); - - inline void copy(const Tensor &ten) noexcept - { cblas_dcopy(ten.get_n_elements(), ten.get_data(), 1, data, 1); }; - - inline long int max_id() noexcept - { return cblas_idamax(n_elements, data, 1); }; +#endif - inline double max() noexcept - { return data[max_id()]; }; +static void Dopnew(double *host_ptr) { operator delete[](host_ptr, alignment); } - inline long int max_id(vector &R) noexcept - { - auto max_id = -1; - auto max = -DBL_MAX; - assert(R.size() == n_elements); +struct Unfolding { + dim_t n_blocks; /*< The number of matrix blocks. */ + dim_t block_offset; /*< The offset (# entries) between blocks in the tensor. */ + dim_t rows; /*< The rows of one block. */ + dim_t cols; /*< The cols of one block. */ + dim_t stride; /*< The (row- or column-) stride of each block. */ +}; - for (auto i = 0lu; i < R.size(); i++) - if (R[i] == true && data[i] > max) - { - max = data[i]; - max_id = i; - } - assert(max_id != -1); - return max_id; - }; +// TODO Create separate class for "data_up" to support an iterator, if it does not inhibit performance. +// or implement iterator for the tensor class to simplify certain C-like loops +class Tensor { + int rank{0} /*< Rank of the tensor (if known) */; + dim_t n_elements{} /*< Total number of elements in tensor */; + dim_t max_n_elements{} /*< Total memory allocated size of the tensor (this cannot change after creation) */; + vector modes; /*< Vector containing the dimensions of each mode of the tensor */ - inline double max(vector &R) noexcept - { - return data[max_id(R)]; - }; +#if CUDA_ENABLED + /* Unique pointer pointing to the data associated with the tensor (RAII) on the GPU */ + mutable unique_ptr cudata_up{nullptr, &Ddev}; + /* Pointer to the contents of the tensor on the GPU. This can be set to point elsewhere from the data owned by the + * tensor (cudata_up) to facilitate "view" type functionality. */ + mutable double *cudata{}; +#endif - inline double min() noexcept - { return *std::min_element(data, data + n_elements); }; + /* Unique pointer pointing to the data associated with the tensor (RAII) */ + unique_ptr data_up{nullptr, &Dopnew}; + + /* Pointer to the contents of the tensor. This can be set to point elsewhere from the data owned by the tensor + * (data_up) to facilitate "view" type functionality. */ + double *data{}; + +public: + Tensor() = default; + + ~Tensor() = default; + + /** Generic constructor that allocates a Tensor (no initialization). + * + * This constructor allocates a Tensor with specific sizes of \p modes. + * The contents of the Tensor created are not initialized. One can use the randomize member function + * to fill in the Tensor with random values. + * + * @param modes A vector containing the sizes of each mode of the Ktensor. + */ + explicit Tensor(const vector &modes); + + /** Constructor that uses pre-existing Tensor. + * + * This constructor creates a Tensor object which points to data (does not own it) already existing in memory. + * It is meant to be used, for example, with Matlab, to view a Tensor already initialized in Matlab. + * + * @param modes A vector containing the sizes of each mode of the Ktensor. + * @param view_data Pointer to the location of the Tensor data. + */ + explicit Tensor(const vector &modes, double *view_data); + + /** Constructor that reads a Tensor from a file. + * + * This constructor creates a Tensor, whose contents are read from a file. + * The first line of the file must contain the dimensions, separated by spaces. + * The rest of the file should contain the elements of the Tensor, separated by the newline character. + * + * @param file_name The name of the file to read the Tensor from. + */ + explicit Tensor(const std::string &file_name); + + /** Constructor that simplifies the creation of Matrices (2D tensors). + * + * @param mode0 Number of rows of a Matrix. + * @param mode1 Number of columns of a Matrix. + * @param view_data (Optional) If set, the Matrix created points to data already existing in memory (does not + * own that data). Otherwise, it allocates an (un-initialized) Matrix. + */ + Tensor(dim_t mode0, dim_t mode1, double *view_data = nullptr); // Constructor to simplify creation of matrices + + /** Creates a Tensor of a specific rank. + * + * This constructor creates a Tensor of a specific rank by first creating a randomly initialized Ktensor + * of a specific \p rank, and then transforms that Ktensor into a full Tensor. + * + * @param rank The desired rank of the Tensor. + * @param modes A vector containing the sizes of each mode of the Ktensor. + */ + Tensor(int rank, const vector &modes); + + // Move and Copy Constructors + Tensor(Tensor &&rhs) = default; + + Tensor &operator=(Tensor &&rhs) = default; + + Tensor(const Tensor &); + + Tensor &operator=(const Tensor &rhs); + + // Getters + [[nodiscard]] inline dim_t get_n_elements() const noexcept { return n_elements; }; + + [[nodiscard]] inline dim_t get_max_n_elements() const noexcept { return max_n_elements; }; + + [[nodiscard]] inline dim_t get_n_modes() const noexcept { return static_cast(modes.size()); }; + + [[nodiscard]] inline vector get_modes() const noexcept { return modes; }; + + [[nodiscard]] inline double *get_data() const noexcept { return data; }; + + [[nodiscard]] inline int get_rank() const noexcept { return rank; }; + + /** Change the data member variable to point to a specific place in memory (other than the owned memory, if it + * exists). + * + * @param new_data pointer to memory where the Tensor should point to. + */ + inline void set_data(double *new_data) noexcept { data = new_data; }; + + /** Reset the data member variable to the memory "owned" by the Matrix (pointed to by the data_up variable). + * + * @return Reference to self. + */ + inline Tensor &reset_data() noexcept { + data = data_up.get(); + return *this; + }; - void print(const std::basic_string& text = "Tensor") const; + /** Check whether the Tensor owns memory (RAII) or is it just a "view". + * + * @return True if the Tensor does not "own" data. + */ + [[nodiscard]] inline bool is_view() const noexcept { return data_up == nullptr; }; + + /** Operator to access an element in the Tensor, giving its index. + * + * @param index the index of the element. + * + * @return The element of the Tensor pointed to by the index. + */ + inline double const &operator[](dim_t index) const noexcept { return data[index]; } + + inline double &operator[](dim_t index) noexcept { return data[index]; } + + /** "Soft" resize, meaning the dimensions of the Tensor are only changed (not the underlying memory) + * + * The new requested sizes must fit inside the memory initially allocated for the Tensor (max_n_elements). + * + * @param new_n_elements new number of total elements. + * @param new_modes new dimensions of the Tensor. + */ + void resize(dim_t new_n_elements, vector &new_modes) { + assert(new_n_elements <= max_n_elements); + assert(new_modes.size() == modes.size()); + + n_elements = new_n_elements; + modes = std::move(new_modes); + } + + /** Compute the L2-norm of the Tensor. + * + * @return L2-norm of the Tensor. + */ + [[nodiscard]] double norm() const { return cblas_dnrm2(n_elements, data, 1); }; + + /** Fill the Tensor with values. + * + * @param f function that returns a double every time it is invoked. + * + * @return Reference to self. + */ + Tensor &fill(const function &&f); + + /** Fill the Tensor with zeros. + * + * @return Reference to self. + */ + Tensor &zero(); + + /** Fill the Tensor with random values (uniform distribution between [-1.0--1.0]). + * + * @param r (Optional) Whether to generate a reproducible set of random numbers (for experiments and testing) + * + * @return Reference to self. + */ + Tensor &randomize(); + + /** Read (and copy) a Tensor. + * + * @param ten Tensor to read. + */ + inline void copy(const Tensor &ten) noexcept { cblas_dcopy(ten.get_n_elements(), ten.get_data(), 1, data, 1); }; + + /** Return the id of the maximum element (consulting a mask). + * + * @param mask vector of booleans specifying which ids to consider in finding the id of the maximum element. + * + * @return the id of the maximum element. + */ + inline dim_t max_id(vector &mask) noexcept { + dim_t max_id = 0; + DEBUG(bool found = false;) + auto max = -DBL_MAX; + assert(mask.size() == n_elements); + + for (auto i = 0lu; i < mask.size(); i++) + if (mask[i] && data[i] > max) { + max = data[i]; + max_id = i; + DEBUG(found = true;) + } + assert(found); + return max_id; + }; - Unfolding implicit_unfold(int mode) const; + /** Return the maximum element (consulting a mask). + * + * @param mask vector of booleans specifying which elements to consider in finding the maximum element. + * + * @return the maximum element of the Tensor. + */ + inline double max(vector &mask) noexcept { return data[max_id(mask)]; }; + + /** Return the minimum element. + * + * @return the minimum element of the Tensor. + */ + inline double min() noexcept { return *std::min_element(data, data + n_elements); }; + + /** Print the contents of the Tensor, together with some optional text. + * + * @param text (Optional) Text to display along with the contents of the Tensor. + */ + void print(const std::string &&text = "Tensor") const; + + /** Calculate the variables associated with performing an MTTKRP without memory movement. + * + * An MTTKRP can be computing by explicitly computing the Khatri-Rao product and multiplying the unfolded Tensor + * with this KRP. This function determines how to perform the multiplication with the Tensor in-place (without + * transposing the Tensor). + * + * @param mode Calculate the implicit Unfolding to perform an MTTKRP for the \p mode. + * + * @return Unfolding object. + */ + [[nodiscard]] Unfolding implicit_unfold(dim_t mode) const; #if CUDA_ENABLED - inline double * const & get_cudata() const noexcept - { return cudata; }; - inline double *& get_cudata() noexcept - { return cudata; }; + inline double *const &get_cudata() const noexcept { return cudata; }; - inline void allocate_cudata(int size) const noexcept - { - cuda::allocate(cudata, size); - cudata_up = unique_ptr(cudata, DDev()); - }; + inline double *&get_cudata() noexcept { return cudata; }; - inline void send_to_device() const noexcept - { cuda::send_to_device(data, cudata, n_elements); }; + inline void allocate_cudata(dim_t size) const noexcept { + cuda::allocate(cudata, size); + cudata_up = unique_ptr(cudata, &Ddev); + }; - inline void receive_from_device() noexcept - { cuda::receive_from_device(cudata, data, n_elements); }; + inline void send_to_device() const noexcept { cuda::send_to_device(data, cudata, n_elements); }; - inline void send_to_device_async(cudaStream_t &stream) const noexcept - { cuda::send_to_device_async(data, cudata, n_elements, stream); }; + inline void receive_from_device() noexcept { cuda::receive_from_device(cudata, data, n_elements); }; - inline void receive_from_device_async(cudaStream_t &stream) noexcept - { cuda::receive_from_device_async(cudata, data, n_elements, stream); }; + inline void send_to_device_async(cudaStream_t &stream) const noexcept { + cuda::send_to_device_async(data, cudata, n_elements, stream); + }; - inline void set_cudata(double *new_cudata) noexcept - { cudata = new_cudata; }; + inline void receive_from_device_async(cudaStream_t &stream) noexcept { + cuda::receive_from_device_async(cudata, data, n_elements, stream); + }; - inline Tensor &reset_cudata() noexcept - { - cudata = cudata_up.get(); - return *this; - }; -#endif + inline void set_cudata(double *new_cudata) noexcept { cudata = new_cudata; }; + inline Tensor &reset_cudata() noexcept { + cudata = cudata_up.get(); + return *this; }; +#endif +}; } // namespace cals diff --git a/include/timer.h b/include/timer.h index 33eb9ae..df98d45 100644 --- a/include/timer.h +++ b/include/timer.h @@ -1,121 +1,101 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #ifndef CALS_TIMER_H #define CALS_TIMER_H #include -#include -#include - -using std::vector; -using time_point = std::chrono::high_resolution_clock::time_point; +#include namespace cals { class Timer { - time_point t0{}; - time_point t1{}; - double t{FLT_MAX}; - double min{FLT_MAX}; + std::chrono::high_resolution_clock::time_point t0{}; + double t{std::numeric_limits::max()}; public: Timer() = default; + ~Timer() = default; + inline void start() { t0 = std::chrono::high_resolution_clock::now(); }; inline void stop() { - t1 = std::chrono::high_resolution_clock::now(); + auto t1 = std::chrono::high_resolution_clock::now(); t = std::chrono::duration_cast(t1 - t0).count() / 1e9; - if (t < min) min = t; }; + inline void reset() + { t = 0.0; }; + inline double get_time() const - { return (t == FLT_MAX) ? 0.0 : t; }; + { return (t == std::numeric_limits::max()) ? 0.0 : t; }; + }; - inline double get_min() const - { return (min == FLT_MAX) ? 0.0 : min; }; + struct MttkrpTimers + { + enum TIMERS + { + MT_KRP = 0, + MT_GEMM, + TS_GEMM, + TS_GEMV, + LENGTH + }; + std::string names[TIMERS::LENGTH] = + { + "MT_KRP", + "MT_GEMM", + "TS_GEMM", + "TS_GEMV" + }; + Timer timers[TIMERS::LENGTH]; + + Timer &operator[](int timer) + { return timers[timer]; } }; - struct ModeTimer + struct ModeTimers { - enum MODE_TIMERS + enum TIMERS { - TOTAL_MTTKRP = 0, - MTTKRP_KRP, - MTTKRP_GEMM, - TWOSTEP_GEMM, - TWOSTEP_GEMV, + MTTKRP = 0, UPDATE, LENGTH }; - std::string names[MODE_TIMERS::LENGTH] = + std::string names[TIMERS::LENGTH] = { "TOTAL_MTTKRP", - "MTTKRP_KRP", - "MTTKRP_GEMM", - "TWOSTEP_GEMM", - "TWOSTEP_GEMV", "UPDATE" }; - Timer timers[MODE_TIMERS::LENGTH]; + Timer timers[TIMERS::LENGTH]; + + Timer &operator[](int timer) + { return timers[timer]; } }; - struct AlsTimer + struct AlsTimers { - enum ALS_TIMERS + enum TIMERS { - TOTAL = 0, - ITERATION, + ITERATION = 0, + DEFRAGMENTATION, ERROR, G_COPY, LENGTH }; - std::string names[ALS_TIMERS::LENGTH] = + std::string names[TIMERS::LENGTH] = { - "TOTAL", "ITERATION", + "DEFRAGMENTATION", "ERROR", "G_COPY" }; - Timer timers[ALS_TIMERS::LENGTH]; + + Timer timers[TIMERS::LENGTH]; + + Timer &operator[](int timer) + { return timers[timer]; } }; } // namespace cals diff --git a/include/utils.h b/include/utils.h deleted file mode 100644 index 052611b..0000000 --- a/include/utils.h +++ /dev/null @@ -1,191 +0,0 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - -#ifndef CALS_UTILS_H -#define CALS_UTILS_H - -#include -#include -#include -#include - -#include "ktensor.h" -#include "timer.h" - -typedef vector> MttkrpLut; - -namespace cals::ops -{ - /* Enumeration of different MTTKRP methods. - * */ - enum MTTKRP_METHOD - { - MTTKRP = 0, /* Compute KRP explicitly and multiply with unfolded tensor. */ - TWOSTEP0, /* Compute MTTKRP in two steps, involving one GEMM and multiple GEMVs. */ - TWOSTEP1, /* Compute MTTKRP in two steps, involving multiple (parallelizable) GEMMs and multiple GEMVs. */ - AUTO /* Let the Lookup Tables (if they exist) or our emprirical algorithm decide. */ - }; - - struct LineSearchParams - { - bool cuda{false}; /* Indicate whether cuda is used (to stream results back to the GPU when done). */ - double step{0.0}; /* Factor with which to move on the line. */ - }; - - struct KrpParams - { - bool cuda{false}; /* Indicate whether cuda is used (to stream results back to the GPU when done). */ - - uint64_t flops{0}; /* Output: Number of FLOPs performed. */ - uint64_t memops{0}; /* Output: Number of MemOps performed. */ - -#if CUDA_ENABLED - cuda::CudaParams cuda_params; -#endif - }; - - struct MttkrpParams - { - MTTKRP_METHOD method{AUTO}; /* Method to use to compute MTTKRP. */ - KrpParams krp_params{}; /* Parameters to be used for the computation of the KRP (if computed). */ - - /* Optional timers to measure time in a fine grained manner. */ - Timer *timer_mttkrp_krp{nullptr}; - Timer *timer_mttkrp_gemm{nullptr}; - - Timer *timer_twostep_gemm{nullptr}; - Timer *timer_twostep_gemv{nullptr}; - - MttkrpLut lut{}; /* Lookup Table containing the "best" method to compute MTTKRP for the size of the tensor, rank of Ktensor and number of threads or GPU. */ - vector lut_keys{}; /* Sorted keys of the Lookup Table, for searching. */ - - u_int64_t flops{0}; /* Output: Number of FLOPs performed. */ - u_int64_t memops{0}; /* Output: Number of MemOps performed. */ - - bool cuda{false}; /* Indicate whether cuda is used (to stream results back to the GPU when done). */ - -#if CUDA_ENABLED - cuda::CudaParams cuda_params{}; -#endif - }; - - /* Function to compute the MTTKRP. - * - * @param X Target Tensor. - * @param u Ktensor to use for MTTKRP. - * @param workspace Vector of Matrix of appropriate size, to be used as workspace. - * @param mode Mode for which to compute the MTTKRP. - * @param params Specific parameters for MTTKRP. - * - * @return Reference to the factor matrix of the /p mode factor Matrix of /p u. - * */ - Matrix &mttkrp(const Tensor &X, Ktensor &u, vector &workspace, int mode, MttkrpParams ¶ms); - - /* Compute the approximation error of the Ktensor based on the FastALS paper formula. - * - * @param X_norm Norm of the target tensor. - * @param lambda Vector of lambdas of Ktensor. - * @params last_factor Last Factor matrix of Ktensor. - * @params last_mttkrp The resulting Matrix of the last MTTKRP performed in the current iteration. - * @params gramian_hadamard Matrix containing the hadamard product of the gramians of the Ktensor. - * - * @return The error of the Ktensor based on the FastALS paper formula. - * */ - double compute_fast_error(double X_norm, const vector &lambda, const Matrix &last_factor, - const Matrix &last_mttkrp, const Matrix &gramian_hadamard); - - void update_gramian(const Matrix &factor, Matrix &gramian); - - // Compute the hadamard product of all but the matrix in position "mode". Store the result in position "mode". - Matrix &hadamard_but_one(vector &gramians, int mode); - - // Compute the hadamard product of all matrices and store the result in the first. - void hadamard_all(vector &gramians); - - void line_search(Ktensor &ktensor, Ktensor &ls_ktensor, Matrix &krp_workspace, Matrix &ten_workspace, - vector &gramians, const Tensor &X, double X_norm, LineSearchParams ¶ms); -} - -namespace cals::update -{ - enum UPDATE_METHOD - { - UNCONSTRAINED = 0, - NNLS, - LENGTH - }; - - static const std::string update_method_names[UPDATE_METHOD::LENGTH] = - { - "unconstrained", - "nnls" - }; - - // Update the factor matrix by solving the SPD system factor * gramian = G - // hint: The factor matrix should initially hold the result of the MTTKRP - Matrix &update_factor_unconstrained(Matrix &factor, Matrix &gramian); - - Matrix &update_factor_non_negative_constrained(Matrix &factor, Matrix &gramian, vector> &active_old); -} - -namespace cals::utils -{ - MttkrpLut read_lookup_table(vector const &modes, int threads); - - template - inline void set_mttkrp_timers(T &rep, cals::ops::MttkrpParams &mttkrp_params, int mode) - { - mttkrp_params.timer_mttkrp_krp = &rep.mode_timers[mode].timers[cals::ModeTimer::MODE_TIMERS::MTTKRP_KRP]; - mttkrp_params.timer_mttkrp_gemm = &rep.mode_timers[mode].timers[cals::ModeTimer::MODE_TIMERS::MTTKRP_GEMM]; - - mttkrp_params.timer_twostep_gemm = &rep.mode_timers[mode].timers[cals::ModeTimer::MODE_TIMERS::TWOSTEP_GEMM]; - mttkrp_params.timer_twostep_gemv = &rep.mode_timers[mode].timers[cals::ModeTimer::MODE_TIMERS::TWOSTEP_GEMV]; - } - - template - inline void set_mttkrp_timers(T &rep, cals::ops::MttkrpParams &mttkrp_params, int mode, int iter) - { - mttkrp_params.timer_mttkrp_krp = &rep.mode_timers[iter][mode].timers[cals::ModeTimer::MODE_TIMERS::MTTKRP_KRP]; - mttkrp_params.timer_mttkrp_gemm = &rep.mode_timers[iter][mode].timers[cals::ModeTimer::MODE_TIMERS::MTTKRP_GEMM]; - - mttkrp_params.timer_twostep_gemm = &rep.mode_timers[iter][mode].timers[cals::ModeTimer::MODE_TIMERS::TWOSTEP_GEMM]; - mttkrp_params.timer_twostep_gemv = &rep.mode_timers[iter][mode].timers[cals::ModeTimer::MODE_TIMERS::TWOSTEP_GEMV]; - } -} - - -#endif //CALS_UTILS_H diff --git a/include/utils/error.h b/include/utils/error.h new file mode 100644 index 0000000..c291223 --- /dev/null +++ b/include/utils/error.h @@ -0,0 +1,61 @@ +#ifndef CP_CALS_ERROR_H +#define CP_CALS_ERROR_H + +#include "ktensor.h" + +namespace cals::error { + +/** Compute the approximation error of the Ktensor based on the FastALS paper formula. + * + * Method implemented is described in the following paper: + * A. Phan, P. Tichavský and A. Cichocki, "Fast Alternating LS Algorithms for High Order CANDECOMP/PARAFAC Tensor + * Factorizations," in IEEE Transactions on Signal Processing, vol. 61, no. 19, pp. 4834-4846, Oct.1, 2013, + * doi: 10.1109/TSP.2013.2269903. + * + * @param X_norm Norm of the target tensor. + * @param lambda Vector of lambdas of Ktensor. + * @param last_factor Last Factor matrix of Ktensor. + * @param last_mttkrp The resulting Matrix of the last MTTKRP performed in the current iteration. + * @param gramian_hadamard Matrix containing the hadamard product of the gramians of the Ktensor. + * + * @return The error of the Ktensor based on the FastALS paper formula. + */ +double compute_fast_error(double X_norm, + const std::vector &lambda, + const cals::Matrix &last_factor, + const cals::Matrix &last_mttkrp, + const cals::Matrix &gramian_hadamard); + +/** Compute the approximation error of the Ktensor by explicitly reconstructing a full tensor, elementwise subtracting + * it from the real tensor \p X and returning the norm. + * + * (This function is used only in line search and thus only supports 3D tensors. The reconstruction is done by + * first calculating the Khatri-Rao Product of factor matrices C and B, which is then multiplied with factor matrix A.) + * + * @param X The target tensor. + * @param ktensor Ktensor for which to measure the approximation error. + * @param krp_workspace Workspace capable of holding the Khatri-Rao product of factor matrices B and C. + * @param ten_workspace Workspace capable of holding the reconstructed tensor. + * + * @return The approximation error of the Ktensor. + */ +double +compute_error(const cals::Tensor &X, cals::Ktensor &ktensor, cals::Matrix &krp_workspace, cals::Matrix &ten_workspace); + +/** Compute the approximation error of the Ktensor by explicitly reconstructing a full tensor, elementwise subtracting + * it from the real tensor \p X and returning the norm. + * + * (This function is used only in line search and thus only supports 3D tensors. The reconstruction is done on the fly, + * without requiring extra workspace, using a custom quadruple loop implementation. It was designed to facilitate + * concurrent line search invocations without running out of workspace memory). + * + * @param X The target tensor. + * @param ktensor Ktensor for which to measure the approximation error. + * @param tr_ktensor Ktensor to be used as workspace (of same size as \p ktensor). + * + * @return The approximation error of the Ktensor. + */ +double compute_error_par(const cals::Tensor &X, cals::Ktensor &ktensor, cals::Ktensor &tr_ktensor); + +} // namespace cals::error +#endif // CP_CALS_ERROR_H diff --git a/include/utils/line_search.h b/include/utils/line_search.h new file mode 100644 index 0000000..101cb25 --- /dev/null +++ b/include/utils/line_search.h @@ -0,0 +1,63 @@ +#ifndef CP_CALS_LINE_SEARCH_H +#define CP_CALS_LINE_SEARCH_H + +#include "ktensor.h" + +namespace cals::ls { +struct LineSearchParams { + bool cuda{false}; /*< Indicate whether cuda is used (to stream results back to the GPU when done). */ + double step{0.0}; /*< Factor with which to extrapolate. */ +}; + +/** Perform line search extrapolation. (only available for 3D tensors) + * + * The ktensor is extrapolated and the new approximation error is calculated (with respect to the target tensor). If the + * new error decreases, substitute the ktensor with the extrapolation. Otherwise, leave the ktensor as is. + * + * @param[in, out] ktensor the Ktensor on which to perform line search. If the error decreases, the \p ktensor is + * updated. + * @param[in] ls_ktensor Ktensor object to temporarily store the extrapolated ktensor. + * @param[in] krp_workspace Workspace of size (at least) [B.rows*C.rows x B.cols] to store the Khatri-rao product for + * the reconstruction (necessary for error calculation). + * @param[in] ten_workspace Workspace of size (at least) [I * J * K] to store the reconstructed tensor (necessary for + * error calculation). + * @param[out] gramians Vector containing the gramian Matrix objects, to be updated in case the \p ktensor is updated. + * @param[in] X Target tensor, used for error calculation. + * @param[in] X_norm L2 norm of the target tensor. + * @param[in] params LineSearchParams object, containing parameters related to the line search extrapolation. + */ +void line_search(cals::Ktensor &ktensor, + cals::Ktensor &ls_ktensor, + cals::Matrix &krp_workspace, + cals::Matrix &ten_workspace, + std::vector &gramians, + const cals::Tensor &X, + double X_norm, + cals::ls::LineSearchParams ¶ms); + +/** Perform line search extrapolation. This version favors concurrent calculation of line search, without high + * memory usage. (only available for 3D tensors) + * + * The ktensor is extrapolated and the new approximation error is calculated (with respect to the target tensor). If the + * new error decreases, substitute the ktensor with the extrapolation. Otherwise, leave the ktensor as is. + * + * @param[in, out] ktensor the Ktensor on which to perform line search. If the error decreases, the \p ktensor is + * updated. + * @param[in] ls_ktensor Ktensor object to temporarily store the extrapolated ktensor. + * @param[in] ls_tr_ktensor Ktensor object to temporarily store the extrapolated ktensor. + * @param[out] gramians Vector containing the gramian Matrix objects, to be updated in case the \p ktensor is updated. + * @param[in] X Target tensor, used for error calculation. + * @param[in] X_norm L2 norm of the target tensor. + * @param[in] params LineSearchParams object, containing parameters related to the line search extrapolation. + */ +void line_search_par(cals::Ktensor &ktensor, + cals::Ktensor &ls_ktensor, + cals::Ktensor &ls_tr_ktensor, + std::vector &gramians, + const cals::Tensor &X, + double X_norm, + cals::ls::LineSearchParams ¶ms); + +} // namespace cals::ls + +#endif // CP_CALS_LINE_SEARCH_H diff --git a/include/utils/mttkrp.h b/include/utils/mttkrp.h new file mode 100644 index 0000000..66b0eb4 --- /dev/null +++ b/include/utils/mttkrp.h @@ -0,0 +1,106 @@ +#ifndef CP_CALS_MTTKRP_H +#define CP_CALS_MTTKRP_H + +#include +#include +#include + +#include "ktensor.h" +#include "matrix.h" +#include "tensor.h" +#include "timer.h" + +namespace cals::mttkrp { +/* Define new type for a vector of lookup tables */ +typedef std::vector> LUT_v; + +struct MttkrpLut { + LUT_v lut_v{}; /*< Lookup Table containing the "best" method to compute MTTKRP for the size of the tensor, rank of + Ktensor and number of threads or GPU. */ + std::vector keys_v{}; /*< Sorted keys of the Lookup Table, for searching. */ +}; + +/* Enumeration of different MTTKRP methods. + * */ +enum MTTKRP_METHOD { + MTTKRP = 0, /*< Compute KRP explicitly and multiply with unfolded tensor. (default choice for >3D tensors) */ + TWOSTEP0, /*< Compute MTTKRP in two steps, involving one GEMM and multiple GEMVs. (only for 3D tensors) */ + TWOSTEP1, /*< Compute MTTKRP in two steps, involving multiple (parallelizable) GEMMs and multiple GEMVs. (only 3D) */ + AUTO, /*< Automatically choose using Lookup Tables (if they exist) or our emprirical algorithm. */ + LENGTH +}; + +static const std::string mttkrp_method_names[MTTKRP_METHOD::LENGTH] = {"MTTKRP", "TWOSTEP0", "TWOSTEP1", "AUTO"}; + +struct KrpParams { + uint64_t flops{0}; /*< Output: Number of FLOPs performed. */ + uint64_t memops{0}; /*< Output: Number of MemOps performed. */ + + bool cuda{false}; /*< Input: Indicate whether cuda is used (to stream results back to the GPU when done). */ + +#if CUDA_ENABLED + cuda::CudaParams cuda_params; /*< Parameters related to CUDA. */ +#endif +}; + +struct MttkrpParams { + MTTKRP_METHOD method{AUTO}; /*< Input: Method to use to compute MTTKRP. */ + KrpParams krp_params{}; /*< Input/Output: Parameters to be used for the computation of the KRP (if computed). */ + + cals::mttkrp::MttkrpLut + lut{}; /*< Input: Lookup table with the best choice of computing the MTTKRP (used in AUTO with 3D tensors only) */ + + bool cuda{false}; /*< Input: Indicate whether cuda is used (to stream results back to the GPU when done). */ + + MttkrpTimers mttkrp_timers; /*< Output: Timer object containing a breakout of timings across the different phases. */ + + u_int64_t flops{0}; /*< Output: Number of FLOPs performed. */ + u_int64_t memops{0}; /*< Output: Number of MemOps performed. */ + +#if CUDA_ENABLED + cuda::CudaParams cuda_params{}; +#endif +}; + +/** Function to compute the MTTKRP. + * + * @param X Target Tensor. + * @param u Ktensor to use for MTTKRP. + * @param workspace Vector of Matrix of appropriate size, to be used as workspace. + * @param mode Mode for which to compute the MTTKRP. + * @param params Specific parameters for MTTKRP. + * + * @return Reference to the factor matrix of the /p mode factor Matrix of /p u. + */ +cals::Matrix &mttkrp(const cals::Tensor &X, + cals::Ktensor &u, + std::vector &workspace, + int mode, + cals::mttkrp::MttkrpParams ¶ms); + +/** Function to compute the Khatri-Rao product. + * + * @param[in] A left Matrix of the KRP. + * @param[in] B right Matrix of the KRP. + * @param[out] workspace workspace Matrix of size (at least) [A.rows*B.rows x B.cols]. + * @param[in, out] params KrpParams object specifying parameters for KRP. + * + * @return Reference to the workspace, where the result was stored. + */ +cals::Matrix & +khatri_rao(const cals::Matrix &A, const cals::Matrix &B, cals::Matrix &workspace, cals::mttkrp::KrpParams ¶ms); + +/** Read a LookUp Table (LUT) from a file. + * + * @param[in] modes The modes of the target tensor. + * @param[in] threads Number of threads available. + * @param[in] gpu (optional) Whether to look for GPU LUT. + * @param[in] suppress_warning (optional) whether to suppress warning if LUT was not found. + * + * @return MttkrpLut object containing the LUT read from the file. + */ +MttkrpLut +read_lookup_table(std::vector const &modes, int threads, bool gpu = false, bool suppress_warning = false); +} // namespace cals::mttkrp + +#endif // CP_CALS_MTTKRP_H diff --git a/include/utils/update.h b/include/utils/update.h new file mode 100644 index 0000000..e85de6a --- /dev/null +++ b/include/utils/update.h @@ -0,0 +1,20 @@ +#ifndef CP_CALS_UPDATE_H +#define CP_CALS_UPDATE_H + +#include "matrix.h" + +namespace cals::update { +enum UPDATE_METHOD { UNCONSTRAINED = 0, NNLS, LENGTH }; + +static const std::string update_method_names[UPDATE_METHOD::LENGTH] = {"unconstrained", "nnls"}; + +// Update the factor matrix by solving the SPD system factor * gramian = G +// hint: The factor matrix should initially hold the result of the MTTKRP +Matrix &update_factor_unconstrained(cals::Matrix &factor, cals::Matrix &gramian); + +Matrix &update_factor_non_negative_constrained(cals::Matrix &factor, + cals::Matrix &gramian, + std::vector> &active_old); +} // namespace cals::update + +#endif // CP_CALS_UPDATE_H diff --git a/include/utils/utils.h b/include/utils/utils.h new file mode 100644 index 0000000..634941e --- /dev/null +++ b/include/utils/utils.h @@ -0,0 +1,46 @@ +#ifndef CALS_UTILS_H +#define CALS_UTILS_H + +#include + +#include "matrix.h" + +namespace cals::utils { +/** Create a string with the modes of a tensor. + * + * @param modes vector containing the modes of the tensor. + * + * @return String with the modes of a tensor. + */ +std::string mode_string(std::vector const &modes); +} // namespace cals::utils + +namespace cals::ops { +/** Compute the gramian by performing an A^{T}A operation. + * + * @param[in] factor Factor Matrix for which to compute the gramian. + * @param[out] gramian Matrix in which to store the gramian. + */ +void update_gramian(const cals::Matrix &factor, cals::Matrix &gramian); + +/** Compute the hadamard product of a set of matrices except one (which will be the output). + * + * Hadamard of the all matrices in \p matrices, except the Matrix in position \p mode. (all matrices are assumed of + * same size. Store the result in position \p mode. + * + * @param[in, out] matrices vector containing Matrix objects to be multiplied together. + * @param[in] mode specify the index of the matrix in \p matrices, in which the output is going to be written. + * + * @return Reference to Matrix used as output. + */ +Matrix &hadamard_but_one(std::vector &matrices, int mode); + +/** Compute the hadamard product of all matrices and store the result in the first. + * + * @param[in, out] matrices vector containing Matrix objects to be multiplied together. The first matrix is used as + * output. + */ +void hadamard_all(std::vector &matrices); +} // namespace cals::ops + +#endif // CALS_UTILS_H diff --git a/matlab/matlab.cpp b/matlab/matlab.cpp index 97b0266..35d0b4f 100644 --- a/matlab/matlab.cpp +++ b/matlab/matlab.cpp @@ -100,11 +100,12 @@ cals::Tensor mxGetTensor(const mxArray *ptr, bool print) DEBUG(int n_elements = mxGetNumberOfElements(data_field);) double *modes = mxGetDoubles(size_field); - int n_modes = mxGetNumberOfElements(size_field); + auto n_modes = mxGetNumberOfElements(size_field); - std::vector modes_vector; + std::vector modes_vector; modes_vector.reserve(n_modes); - for (int i = 0; i < n_modes; i++) modes_vector.push_back(modes[i]); + for (int i = 0; i < n_modes; i++) + modes_vector.push_back(static_cast(modes[i])); assert(std::accumulate(modes_vector.cbegin(), modes_vector.cend(), 1lu, std::multiplies<>()) == n_elements); @@ -117,14 +118,14 @@ cals::Tensor mxGetTensor(const mxArray *ptr, bool print) mxArray *mxSetKtensor(const cals::Ktensor &ktensor) { - const int n_modes = ktensor.get_n_modes(); + const auto n_modes = ktensor.get_n_modes(); // Create factor matrix array (cell array in matlab) mxArray *cell_array_ptr = mxCreateCellMatrix((mwSize) n_modes, (mwSize) 1); - for (int i = 0; i < n_modes; i++) + for (dim_t i = 0; i < n_modes; i++) { - const int rows = ktensor.get_factor(i).get_rows(); - const int cols = ktensor.get_factor(i).get_cols(); + const auto rows = ktensor.get_factor(i).get_rows(); + const auto cols = ktensor.get_factor(i).get_cols(); mxArray *mat_ptr = mxCreateDoubleMatrix((mwSize) rows, (mwSize) cols, mxREAL); double *mat = mxGetDoubles(mat_ptr); cals::Matrix(rows, cols, mat).copy(ktensor.get_factor(i)); @@ -132,7 +133,7 @@ mxArray *mxSetKtensor(const cals::Ktensor &ktensor) } // Create lambda array - mxArray *lambda_ptr = mxCreateDoubleMatrix((mwSize) ktensor.get_rank(), (mwSize) 1, mxREAL); + mxArray *lambda_ptr = mxCreateDoubleMatrix((mwSize) ktensor.get_components(), (mwSize) 1, mxREAL); double *lambda = mxGetDoubles(lambda_ptr); int i = 0; for (const auto &l : ktensor.get_lambda()) lambda[i++] = l; @@ -152,14 +153,14 @@ cals::Ktensor mxGetKtensor(const mxArray *ptr, bool print) // Get lambda array mxArray *lambda_field = mxGetField(ptr, 0, "lambda"); double *lambda = mxGetDoubles(lambda_field); - int rank = mxGetNumberOfElements(lambda_field); + auto rank = mxGetNumberOfElements(lambda_field); // Get Cell array of factor matrices mxArray *u_field = mxGetField(ptr, 0, "u"); - int n_modes = mxGetNumberOfElements(u_field); + auto n_modes = mxGetNumberOfElements(u_field); // Create and initialize modes vector - std::vector modes; + std::vector modes; modes.reserve(n_modes); for (int i = 0; i < n_modes; ++i) { @@ -168,7 +169,7 @@ cals::Ktensor mxGetKtensor(const mxArray *ptr, bool print) } // Create Ktensor - cals::Ktensor ktensor(rank, modes); + cals::Ktensor ktensor(static_cast(rank), modes); // Copy lambda ktensor.set_lambda(lambda); @@ -199,7 +200,7 @@ std::string mxGetStdString(const mxArray *ptr) std::vector mxBuildArgList(int nargs, int offset, const mxArray *margs[]) { - std::vector args(nargs - offset); + std::vector args(static_cast(nargs - offset)); for (int i = 0; i < nargs - offset; i++) { const mxArray *arg = margs[i + offset]; diff --git a/matlab/matlab_parsing.cpp b/matlab/matlab_parsing.cpp index fa0b3d6..5830e44 100644 --- a/matlab/matlab_parsing.cpp +++ b/matlab/matlab_parsing.cpp @@ -217,10 +217,10 @@ namespace cals::matlab::parsing cals::matlab::parsing::parse_enum(args, "update-method", params.update_method, 2, update_types, update_names); // Read MTTKRP method - const cals::ops::MTTKRP_METHOD mttkrp_types[] = {cals::ops::MTTKRP_METHOD::MTTKRP, - cals::ops::MTTKRP_METHOD::TWOSTEP0, - cals::ops::MTTKRP_METHOD::TWOSTEP1, - cals::ops::MTTKRP_METHOD::AUTO}; + const cals::mttkrp::MTTKRP_METHOD mttkrp_types[] = {cals::mttkrp::MTTKRP_METHOD::MTTKRP, + cals::mttkrp::MTTKRP_METHOD::TWOSTEP0, + cals::mttkrp::MTTKRP_METHOD::TWOSTEP1, + cals::mttkrp::MTTKRP_METHOD::AUTO}; const char *mttkrp_names[] = {"mttkrp", "twostep0", "twostep1", "auto"}; params.mttkrp_method = cals::matlab::parsing::parse_enum(args, "mttkrp-method", params.mttkrp_method, 4, mttkrp_types, mttkrp_names); diff --git a/matlab/matlab_src/TTB_benchmark.m b/matlab/matlab_src/TTB_benchmark.m deleted file mode 100644 index e3c22d1..0000000 --- a/matlab/matlab_src/TTB_benchmark.m +++ /dev/null @@ -1,84 +0,0 @@ -% Path to TTB -home = getenv('HOME'); -addpath(home + "/projects/tensor_toolbox/"); -addpath(home + "/projects/TensorFactorizations/data/"); - -n = maxNumCompThreads(); -if n ~= 1 - n = maxNumCompThreads(str2num(getenv('OMP_NUM_THREADS'))); -end -n = maxNumCompThreads(); -%if n == 24 -% maxNumCompThreads(12) -% n = maxNumCompThreads -% if n ~= 12 -% disp("error with number of threads") -% exit -% end -if n == 1 - disp("Threads = 1") -elseif n == 12 - disp("Threads = 12") -elseif n == 24 - disp("Threads = 24") -else - fprintf('Unrecognized number of threads %d\n', n); - exit -end - -clc -clear all -echo off -rand('state', 0) - -mode1 = 299; -mode2 = 301; -mode3 = 41; -rank_min = 1; -rank_max = 20; -copies = 20; - -tic; -load('fluorescence_data.mat') -X = X_UD.data; -X(isnan(X))=0; -X = tensor(X); -% X = tensor(rand(mode1, mode2, mode3)); -%X = full(ktensor({rand(mode1, tensor_rank), rand(mode2, tensor_rank), rand(mode3, tensor_rank)})); - -ranks = zeros(1, 400); -k = 1; -for i = rank_min:rank_max - for j = 1:copies - ranks(k) = i; - k = k + 1; - end -end -rank_sum = sum(ranks); -fprintf('Ktensors: %d\n', size(ranks, 2)); -fprintf('Ktensor rank sum: %d\n', rank_sum); - -ktensors = {}; -for r = 1:size(ranks, 2) - ktensors{r} = ktensor({rand(mode1, ranks(r)), - rand(mode2, ranks(r)), - rand(mode3, ranks(r))}); -end -time_gen = toc; -fprintf('Time to generate tensor and models: %f\n', time_gen); - -M = {}; -disp(' ') -disp('START TTB ALS') - -timings = zeros(1, size(ktensors, 2)); -for k = 1:size(ktensors, 2) - tic - M{k} = cp_als(X, size(ktensors{k}.lambda, 1), 'maxiters', 50, 'tol', 1e-4, 'init', ktensors{k}.U); - timings(k)= toc; -end -path = "../../data/MKL/TTB_" + string(mode1) + "-" + string(mode2) + "-" + string(mode3) + "_" + string(maxNumCompThreads) + ".csv" -fileID = fopen(path, 'w'); -fprintf(fileID, '%10f\n', timings); - -disp('STOP TTB ALS') diff --git a/matlab/matlab_src/TTB_experiment.m b/matlab/matlab_src/TTB_experiment.m new file mode 100644 index 0000000..6b9f7fe --- /dev/null +++ b/matlab/matlab_src/TTB_experiment.m @@ -0,0 +1,149 @@ +% Path to TTB +home = getenv('HOME'); +addpath(home + "/projects/tensor_toolbox/"); +addpath(home + "/projects/CP-CALS-priv/data/"); + +n = maxNumCompThreads(); +if n ~= 1 + n = maxNumCompThreads(str2num(getenv('OMP_NUM_THREADS'))); +end +n = maxNumCompThreads(); +%if n == 24 +% maxNumCompThreads(12) +% n = maxNumCompThreads +% if n ~= 12 +% disp("error with number of threads") +% exit +% end +if n == 1 + disp("Threads = 1") +elseif n == 24 + disp("Threads = 24") +else + fprintf('Unrecognized number of threads %d\n', n); + exit +end + +clc +clear all +echo off +rand('state', 0) + +rank_min = 1; +rank_max = 20; +copies = 20; + +%% EEMData +tic; +load('eemdata.mat') +X = fluor.data(:,:,:,1); +sizes = size(X); +mode1 = sizes(1); +mode2 = sizes(2); +mode3 = sizes(3); + +X(isnan(X))=0; +X = tensor(X); +% X = tensor(rand(mode1, mode2, mode3)); +%X = full(ktensor({rand(mode1, tensor_rank), rand(mode2, tensor_rank), rand(mode3, tensor_rank)})); + +ranks = zeros(1, 400); +k = 1; +for i = rank_min:rank_max + for j = 1:copies + ranks(k) = i; + k = k + 1; + end +end +rank_sum = sum(ranks); +fprintf('Ktensors: %d\n', size(ranks, 2)); +fprintf('Ktensor rank sum: %d\n', rank_sum); + +ktensors = {}; +for r = 1:size(ranks, 2) + ktensors{r} = ktensor({rand(mode1, ranks(r)), + rand(mode2, ranks(r)), + rand(mode3, ranks(r))}); +end +time_gen = toc; +fprintf('Time to generate tensor and models: %f\n', time_gen); + +M = {}; +disp(' ') +disp('START TTB ALS') + +timings = zeros(1, size(ktensors, 2)); +for k = 1:size(ktensors, 2) + tic + M{k} = cp_als(X, size(ktensors{k}.lambda, 1), 'maxiters', 1000, 'tol', 1e-6, 'init', ktensors{k}.U); + disp(k); + timings(k)= toc; +end +path = "../../data/MKL/TTB_" + string(mode1) + "-" + string(mode2) + "-" + string(mode3) + "_" + string(maxNumCompThreads) + ".csv" +fileID = fopen(path, 'w'); +fprintf(fileID, '%10f\n', timings); + +disp('STOP TTB ALS') + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +clc +clear all +echo off +rand('state', 0) + +rank_min = 1; +rank_max = 20; +copies = 20; + +%% Cancer Data +tic; +load('fluorescence_data.mat') +X = X_UD.data; +sizes = size(X); +mode1 = sizes(1); +mode2 = sizes(2); +mode3 = sizes(3); + +X(isnan(X))=0; +X = tensor(X); +% X = tensor(rand(mode1, mode2, mode3)); +%X = full(ktensor({rand(mode1, tensor_rank), rand(mode2, tensor_rank), rand(mode3, tensor_rank)})); + +ranks = zeros(1, 400); +k = 1; +for i = rank_min:rank_max + for j = 1:copies + ranks(k) = i; + k = k + 1; + end +end +rank_sum = sum(ranks); +fprintf('Ktensors: %d\n', size(ranks, 2)); +fprintf('Ktensor rank sum: %d\n', rank_sum); + +ktensors = {}; +for r = 1:size(ranks, 2) + ktensors{r} = ktensor({rand(mode1, ranks(r)), + rand(mode2, ranks(r)), + rand(mode3, ranks(r))}); +end +time_gen = toc; +fprintf('Time to generate tensor and models: %f\n', time_gen); + +M = {}; +disp(' ') +disp('START TTB ALS') + +timings = zeros(1, size(ktensors, 2)); +for k = 1:size(ktensors, 2) + tic + M{k} = cp_als(X, size(ktensors{k}.lambda, 1), 'maxiters', 1000, 'tol', 1e-6, 'init', ktensors{k}.U); + timings(k)= toc; +end +path = "../../data/MKL/TTB_" + string(mode1) + "-" + string(mode2) + "-" + string(mode3) + "_" + string(maxNumCompThreads) + ".csv" +fileID = fopen(path, 'w'); +fprintf(fileID, '%10f\n', timings); + +disp('STOP TTB ALS') diff --git a/scripts/Lookup Table Generator/generate_LUTs.py b/scripts/Lookup Table Generator/generate_LUTs.py deleted file mode 100644 index 49373b3..0000000 --- a/scripts/Lookup Table Generator/generate_LUTs.py +++ /dev/null @@ -1,67 +0,0 @@ -from system_config import * -from utils import * - -import pandas as pd -import os -import os.path -from pathlib import Path - - -def read_bench(backend, threads, modes, mttkrp_method): - bench_dict = {} - folder = input_path + '{}/benchmark/'.format(backend) - mode_name = mode_string(modes) - bench_file_name = '{}benchmark_{}_{}_{}_{}.csv'.format(folder, backend, mode_name, threads, mttkrp_method) - if os.path.isfile(bench_file_name): - print('Found file: ', bench_file_name) - bench_dict.update({'backend': backend, - 'modes': modes, - 'threads': threads, - 'mttkrp_method': mttkrp_method, - 'data': pd.read_csv(bench_file_name, delimiter=';')}) - return bench_dict - - -def best_method_per_rank(backend, modes, threads): - mttkrp_methods = [MttkrpMethod.MTTKRP, MttkrpMethod.TWOSTEP0, MttkrpMethod.TWOSTEP1] - best = [] - - for id, method in enumerate(mttkrp_methods): - get_best_method(best, read_bench(backend, threads, modes, method), method) - - export_to_file(backend, modes, threads, best) - - -def export_to_file(backend, modes, threads, best): - path_to_files = lut_output_path + backend + '/lookup_tables/' + mode_string(modes) + '/' + str(threads) - print('Path to files: {}'.format(path_to_files)) - - Path(path_to_files).mkdir(parents=True, exist_ok=True) - - for idx, mode in enumerate(modes): - f = open(path_to_files + '/' + str(idx), "w") - for entry in best[idx]: - f.write('{} {}\n'.format(entry[0], entry[3])) - - -def get_best_method(best, bench_dict, method): - dic = bench_dict - - for midx, mode in enumerate(dic['modes']): - - df = dic['data'] - dm = df.loc[df['MODE'] == midx] - if len(best) < midx + 1: - best.append([(e['RANK'], e['FLOPS'], e['TIME'], int(method)) for i, e in dm.iterrows()]) - else: - for i, e in dm.reset_index().iterrows(): - if best[midx][i][1] / best[midx][i][2] < e['FLOPS'] / e['TIME']: - best[midx][i] = (e['RANK'], e['FLOPS'], e['TIME'], int(method)) - - -if __name__ == '__main__': - - backend = 'MKL' - for modes in [(100, 100, 100), (200, 200, 200), (300, 300, 300)]: - for threads in [1, 12, 24]: - best_method_per_rank(backend, modes, threads) diff --git a/scripts/Lookup Table Generator/system_config.py b/scripts/Lookup Table Generator/system_config.py deleted file mode 100644 index 1b3131e..0000000 --- a/scripts/Lookup Table Generator/system_config.py +++ /dev/null @@ -1,18 +0,0 @@ -import copy - -# CLAIX18 (Turbo - SKYLAKE xeon platinum 8160) -CPU_MHZ = {'1': 3.5, '12': 2.6, '24': 2} -CPU_FPC = 32 -CPU_FPS = copy.deepcopy(CPU_MHZ) -for k, v in CPU_FPS.items(): - CPU_FPS[k] = v * CPU_FPC * 1e9 * int(k) - -# Node 72 (No Turbo - HASWELL) -# CPU_MHZ = {'1': 2.5, '12': 2.5} -# CPU_FPC = 16 -# CPU_FPS = CPU_MHZ -# for k, v in CPU_FPS: -# CPU_FPS[k] = v * CPU_FPC * 1e9 * int(k) - -input_path = '../../data/' -lut_output_path = '../../data/' diff --git a/scripts/Lookup Table Generator/utils.py b/scripts/Lookup Table Generator/utils.py deleted file mode 100644 index 0ad39e5..0000000 --- a/scripts/Lookup Table Generator/utils.py +++ /dev/null @@ -1,13 +0,0 @@ -from enum import IntEnum - -class MttkrpMethod(IntEnum): - MTTKRP = 0 - TWOSTEP0 = 1 - TWOSTEP1 = 2 - AUTO = 3 - -def mode_string(modes): - mode_name = "" - for m in modes: - mode_name += str(m) + '-' - return mode_name[:-1] diff --git a/scripts/environment_setup.sh b/scripts/environment_setup.sh new file mode 100755 index 0000000..bdbc778 --- /dev/null +++ b/scripts/environment_setup.sh @@ -0,0 +1,33 @@ +#! /bin/bash + +SCRIPT=$(readlink -f "$0") +SCRIPTPATH=$(dirname "$SCRIPT") +CALS_REPO_PATH="${SCRIPTPATH}/.." + +pushd . > /dev/null +echo -n "Checking ${CALS_REPO_PATH} for extern folder... " +cd ${CALS_REPO_PATH}/extern || (echo " " && echo "Can't find 'extern' folder. Exiting." && popd && exit) + +echo "Found." + +if [ ! -d "cmake" ]; then + echo -n "Previous installation not found. Installing cmake to `pwd` ... " + mkdir cmake + wget https://github.com/Kitware/CMake/releases/download/v3.17.5/cmake-3.17.5-Linux-x86_64.tar.gz -qO- | tar xzf - -C cmake + echo "Done" +fi + +CMAKE_BIN_PATH=`pwd`/cmake/cmake-3.17.5-Linux-x86_64/bin +echo +echo "Copy the following line to your ${HOME}/.bashrc or ${HOME}/.zshrc to be able to find CMake 3.17.5 in a new terminal session:" +echo "export PATH=${CMAKE_BIN_PATH}:\${PATH}" +echo +echo -n "Adding ${CMAKE_BIN_PATH} to PATH variable... " +export PATH=${CMAKE_BIN_PATH}:${PATH} +echo "Done." + +popd > /dev/null + + + + diff --git a/scripts/python/__init__.py b/scripts/python/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scripts/python/experiments/__init__.py b/scripts/python/experiments/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scripts/python/experiments/data_reader.py b/scripts/python/experiments/data_reader.py new file mode 100644 index 0000000..b4df65f --- /dev/null +++ b/scripts/python/experiments/data_reader.py @@ -0,0 +1,125 @@ +import logging +import os.path + +import numpy as np +import pandas as pd + +from scripts.python.utils.system_config import input_path +from scripts.python.utils.utils import modes_string + +logger = logging.getLogger('Data reader') + + +def read_cuda_csv(file_name): + if os.path.isfile(file_name): + return pd.read_csv(file_name, delimiter=';') + else: + return [] + + +def read_ttb(file_name): + if os.path.isfile(file_name): + x = [] + file = open(file_name, 'r') + for y in file.readlines(): + if y: + x.append(float(y)) + return x + else: + return [] + + +def read_data(backend, threads, modes, suffix=None): + results_dict = {} + folder = input_path + '{}/'.format(backend) + mode_name = modes_string(modes) + if suffix is None: + suffix = '' + else: + suffix = '_{}'.format(suffix) + + als_file_name = '{}ALS_{}_{}_{}{}.csv'.format(folder, backend, mode_name, threads, suffix) + als_omp_file_name = '{}ALS_OMP_{}_{}_{}{}.csv'.format(folder, backend, mode_name, threads, suffix) + als_cuda_file_name = '{}ALS_CUDA_{}_{}_{}{}.csv'.format(folder, backend, mode_name, threads, suffix) + als_omp_cuda_file_name = '{}ALS_OMP_CUDA_{}_{}_{}{}.csv'.format(folder, backend, mode_name, threads, suffix) + cals_file_name = '{}CALS_{}_{}_{}{}.csv'.format(folder, backend, mode_name, threads, suffix) + cals_cuda_file_name = '{}CALS_CUDA_{}_{}_{}{}.csv'.format(folder, backend, mode_name, threads, suffix) + ttb_file_name = '{}TTB_{}_{}.csv'.format(folder, modes_string(modes), threads) + + if not os.path.isfile(als_file_name): + logger.warning('File: {} not found.'.format(als_file_name)) + alsdata = [] + else: + alsdata = pd.read_csv(als_file_name, delimiter=';') + if not os.path.isfile(als_omp_file_name): + logger.warning('File: {} not found.'.format(als_omp_file_name)) + alsompdata = [] + else: + alsompdata = pd.read_csv(als_omp_file_name, delimiter=';') + if not os.path.isfile(als_cuda_file_name): + logger.warning('File: {} not found.'.format(als_cuda_file_name)) + alscudadata = [] + else: + alscudadata = pd.read_csv(als_cuda_file_name, delimiter=';') + if not os.path.isfile(als_omp_cuda_file_name): + logger.warning('File: {} not found.'.format(als_omp_cuda_file_name)) + alsompcudadata = [] + else: + alsompcudadata = pd.read_csv(als_omp_cuda_file_name, delimiter=';') + if not os.path.isfile(cals_file_name): + logger.warning('File: {} not found.'.format(cals_file_name)) + calsdata = [] + else: + calsdata = pd.read_csv(cals_file_name, delimiter=';') + if not os.path.isfile(cals_cuda_file_name): + logger.warning('File: {} not found.'.format(cals_cuda_file_name)) + calscudadata = [] + else: + calscudadata = read_cuda_csv(cals_cuda_file_name) + if not os.path.isfile(ttb_file_name): + logger.warning('File: {} not found.'.format(ttb_file_name)) + ttbdata = [] + else: + ttbdata = read_ttb(ttb_file_name) + + results_dict.update({'backend': backend, + 'threads': threads, + 'modes': modes, + 'alsdata': alsdata, + 'alsompdata': alsompdata, + 'alscudadata': alscudadata, + 'alsompcudadata': alsompcudadata, + 'calsdata': calsdata, + 'calscudadata': calscudadata, + 'ttbdata': ttbdata}) + return results_dict + + +def extract_als_data(df): + total_time = [] + comp_time = [] + flops = [] + ranks = [] + it_time = [] + + for _, row in df.iterrows(): + total_time.append(row['TOTAL']) + flops.append(row['FLOPS'] * row['ITER']) + ranks.append(row['KTENSOR_RANK']) + it_time.append(row['ITERATION'] * row['ITER']) + + comp_sum = 0.0 + for m in range(len(row['TENSOR_MODES'].split('-'))): + comp_sum += row['MODE_{}_TOTAL_MTTKRP'.format(m)] + row['MODE_{}_UPDATE'.format(m)] + comp_sum += row['ERROR'] + comp_sum *= row['ITER'] + comp_time.append(comp_sum) + + time_diff = (np.array(total_time) - np.array(comp_time)) / total_time + # print('------- Begin ALS reading ------------------------'); + # print('ALS comp VS total min: {0:.0%}'.format(np.min(time_diff))) + # print('ALS comp VS total mean: {0:.0%}'.format(np.mean(time_diff))) + # print('ALS comp VS total max: {0:.0%}'.format(np.max(time_diff))) + # print('ALS comp VS total std: {0:.0%}'.format(np.std(time_diff))) + # print('------- End ALS reading ------------------------'); + return np.array(total_time), np.array(comp_time), np.array(flops), np.array(ranks), np.array(it_time) diff --git a/scripts/python/experiments/plot_figure_1.py b/scripts/python/experiments/plot_figure_1.py new file mode 100644 index 0000000..9f95342 --- /dev/null +++ b/scripts/python/experiments/plot_figure_1.py @@ -0,0 +1,42 @@ +import matplotlib.pyplot as plt + +from scripts.python.mttkrp.cals_mttkrp_analysis import plot_best_mttkrp +from scripts.python.mttkrp.generate_LUTs import best_method_per_rank +from scripts.python.utils.figure_config import fig_format, fig_size_in, f_latex +from scripts.python.utils.system_config import plot_output_path +from scripts.python.utils.utils import modes_string + +if __name__ == '__main__': + + backend = 'MKL' + modes = (300, 300, 300) + + f_xlabel = True + f_ylabel = True + f_legend = True + + fig, ax = plt.subplots(1, 1) + fig.set_size_inches(w=fig_size_in['width'] - 0.2 * fig_size_in['width'], h=3) + + threads = 1 + best = best_method_per_rank(backend, modes, threads) + ax = plot_best_mttkrp(backend, modes, threads, best, ax, 'C0', + f_ylabel=f_ylabel, f_xlabel=f_xlabel, f_legend=f_legend) + threads = 12 + best = best_method_per_rank(backend, modes, threads) + ax = plot_best_mttkrp(backend, modes, threads, best, ax, 'C1', + f_ylabel=f_ylabel, f_xlabel=f_xlabel, f_legend=f_legend) + threads = 24 + best = best_method_per_rank(backend, modes, threads) + ax = plot_best_mttkrp(backend, modes, threads, best, ax, 'C3', + f_ylabel=f_ylabel, f_xlabel=f_xlabel, f_legend=f_legend) + + fig.tight_layout() + fig.savefig(plot_output_path + + 'MTTKRP_Best_Benchmark_' + + backend + + '_modes_' + modes_string(modes) + + '_sec_3' + + fig_format) + + plt.show() diff --git a/scripts/python/experiments/plot_figure_3.py b/scripts/python/experiments/plot_figure_3.py new file mode 100644 index 0000000..efde029 --- /dev/null +++ b/scripts/python/experiments/plot_figure_3.py @@ -0,0 +1,102 @@ +import logging + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from scripts.python.experiments.data_reader import read_data, extract_als_data +from scripts.python.utils.figure_config import markersize, linewidth, colors, fig_size_in, fig_format +from scripts.python.utils.system_config import plot_output_path +from scripts.python.utils.utils import modes_title_string + +logger = logging.getLogger('Figure 3') + +logging.basicConfig(level=logging.INFO) + +def speedup_plot(backend, threads, modes, ax, count): + x = np.arange(1, 21, 1) + y = [] + yc = [] + for r in range(1, 21): + dic = read_data(backend, threads, modes, 'speedup_{}'.format(r)) + + als = dic['alsdata'] + cals = dic['calsdata'] + cals_cuda = dic['calscudadata'] + + tcals = cals['ITERATION'].sum() + logger.info("Total CALS time: {}".format(tcals)) + + ttime, _, _, _, _ = extract_als_data(als) + tals = np.sum(ttime) + logger.info("Total ALS time: {}".format(tals)) + + y.append(tals / tcals) + + if threads == 24 and isinstance(cals_cuda, pd.DataFrame): + tccals = cals_cuda['TOTAL'].max() + yc.append(tals / tccals) + + if threads == 1: + label = '1 thread' + else: + label = '{} threads'.format(threads) + + yticks = [1] + if not yc: + yticks.extend(list(np.arange(3, np.max(np.array(y)) + 3, 2))) + else: + yticks.extend(list(np.arange(3, np.max(np.array(yc)) + 3, 2))) + yticks = np.array(yticks) + + if not yc: + max_y = np.max(np.array(ax.get_yticks())) + if np.max(np.array(yticks)) > max_y: + ax.set_yticks(yticks) + ax.set_yticklabels([str(i) for i in list(yticks)]) + ax.set_ylim([0, np.max(np.array(y)) + 0.1 * np.max(np.array(y))]) + ax.set_ylabel('Speedup') + else: + ax.set_yscale('log') + yticks = [1, 10, 100] + ax.set_yticks(yticks) + ax.set_yticklabels([str(i) for i in yticks]) + ax.set_ylim([0.9, 110]) + ax.set_ylabel('Speedup') + + if modes == (300, 300, 300): + xticks = [1, 5, 10, 15, 20] + ax.set_xticks(xticks) + ax.set_xticklabels([str(i) for i in list(xticks)]) + ax.set_xlabel('Components') + + ax.set_title(modes_title_string(dic['modes'])) + + ax.grid(b=True, which='both', axis='y') + ax.plot(x, y, '-o', color=colors[count], label=label, markersize=markersize, linewidth=linewidth) + if yc: + ax.plot(x, yc, '-o', color='C2', label='CUDA', markersize=markersize, linewidth=linewidth) + + if modes == (100, 100, 100): + ax.legend(ncol=2) + + return ax + + +if __name__ == '__main__': + + backend = 'MKL' + + fig, ax = plt.subplots(3, 1, sharex='all') + fig.set_size_inches(w=fig_size_in['width'], h=5) + for im, modes in enumerate([(100, 100, 100), (200, 200, 200), (300, 300, 300)]): + for i, threads in enumerate([1, 24]): + speedup_plot(backend, threads, modes, ax[im], i) + ax[im].axhline(1, ls='-', color='red', markersize=markersize, linewidth=linewidth) + plt.tight_layout() + plt.subplots_adjust(wspace=0.23) + fig.savefig(plot_output_path + + 'Speedup_' + + backend + + fig_format) + plt.show() diff --git a/scripts/python/experiments/plot_figure_4.py b/scripts/python/experiments/plot_figure_4.py new file mode 100644 index 0000000..bc11fb6 --- /dev/null +++ b/scripts/python/experiments/plot_figure_4.py @@ -0,0 +1,51 @@ +import matplotlib.pyplot as plt + +from scripts.python.mttkrp.cals_mttkrp_analysis import plot_best_mttkrp +from scripts.python.mttkrp.generate_LUTs import best_method_per_rank +from scripts.python.utils.figure_config import fig_format, fig_size_in, f_latex +from scripts.python.utils.system_config import plot_output_path + +if __name__ == '__main__': + + backend = 'MKL' + + fig, ax = plt.subplots(3, 1, sharex='all') + fig.set_size_inches(w=fig_size_in['width'] - 0.2 * fig_size_in['width'], h=5) + f_xlabel = False + f_ylabel = True + f_legend = False + for im, modes in enumerate([(100, 100, 100), (200, 200, 200), (300, 300, 300)]): + + if modes == (300, 300, 300): + f_xlabel = True + else: + f_xlabel = False + + if modes == (200, 200, 200): + f_legend = True + else: + f_legend = False + + threads = 1 + best = best_method_per_rank(backend, modes, threads) + ax[im] = plot_best_mttkrp(backend, modes, threads, best, ax[im], 'C0', + f_ylabel=f_ylabel, f_xlabel=f_xlabel, f_legend=f_legend) + + threads = 12 + best = best_method_per_rank(backend, modes, threads) + ax[im] = plot_best_mttkrp(backend, modes, threads, best, ax[im], 'C1', + f_ylabel=f_ylabel, f_xlabel=f_xlabel, f_legend=f_legend) + + threads = 24 + best = best_method_per_rank(backend, modes, threads) + ax[im] = plot_best_mttkrp(backend, modes, threads, best, ax[im], 'C3', + f_ylabel=f_ylabel, f_xlabel=f_xlabel, f_legend=f_legend) + + fig.tight_layout() + plt.subplots_adjust(wspace=0.23) + fig.savefig(plot_output_path + + 'MTTKRP_Best_Benchmark_' + + backend + + fig_format) + + plt.show() diff --git a/scripts/python/experiments/plot_figure_5.py b/scripts/python/experiments/plot_figure_5.py new file mode 100644 index 0000000..6240050 --- /dev/null +++ b/scripts/python/experiments/plot_figure_5.py @@ -0,0 +1,152 @@ +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from scripts.python.experiments.data_reader import read_data, extract_als_data +from scripts.python.utils.figure_config import fig_format, fig_size_in, markersize, linewidth +from scripts.python.utils.system_config import plot_output_path, CPU_FPS, GEMM +from scripts.python.utils.utils import modes_string, modes_title_string, plot_gemm + + +def plot_x_ranks(ax, als_df): + ttime, ctime, flops, ranks, ittime = extract_als_data(als_df) + flop_cumsum_als = np.cumsum(flops) + ranks = list(ranks) + x = [] + for i in range(1, 21): + x.append(flop_cumsum_als[ranks.index(i)]) + for el in x: + ax.axvline(el, ymax=0.04, c='g', linewidth=linewidth) + + +def performance_plot_both(dic, ax=None, print_all=False): + als_df = dic['alsdata'] + als_omp = dic['alsompdata'] + cals_df = dic['calsdata'] + ccals_df = dic['ccalsdata'] + ttb_l = dic['ttbdata'] + + if ax is None: + fig, ax = plt.subplots(1, 1) + fig.set_size_inches(w=4.68596, h=3.5) + + threads = str(cals_df['NUM_THREADS'][0]) + + mfps = CPU_FPS[threads] + gemm = GEMM[modes_string(dic['modes'])][str(threads)] + + ttime, ctime, flops, ranks, ittime = extract_als_data(als_df) + flop_cumsum_als = np.cumsum(flops) + + ttime_omp, ctime_omp, flops_omp, ranks_omp, ittime_omp = extract_als_data(als_omp) + flop_cumsum_als_omp = np.cumsum(flops_omp) + + flop_cumsum_cals = cals_df['FLOPS'].cumsum() + + print() + print('CALS Flops: {:>14}, Total: {:>8.2f}, Iteration sum: {:>8.2f}'.format(list(flop_cumsum_cals)[-1], + cals_df['ITERATION'].sum(), + cals_df['TOTAL'].max())) + print('OALS Flops: {:>14}, Total: {:>8.2f}'.format(list(flop_cumsum_als_omp)[-1], + ttime_omp.max())) + print(' ALS Flops: {:>14}, Total: {:>8.2f}, Iteration sum: {:>8.2f}'.format(list(flop_cumsum_als)[-1], + ittime.sum(), + ttime.sum())) + print() + + ax.step(flop_cumsum_cals, cals_df['FLOPS'] / cals_df['ITERATION'] / mfps, + '-', label='CALS', color='C0', markersize=markersize, linewidth=linewidth) + + print('{} {} {} {}'.format(flops_omp[-1], ttime_omp.max(), mfps, flops_omp[-1] / ttime_omp.max() / mfps)) + + if threads != '1': + val = flop_cumsum_als_omp[-1] / ttime_omp.max() / mfps + ax.step([flop_cumsum_als_omp[0], flop_cumsum_als_omp[-1]], + [val, val], + '-', label='OMP ALS', color='C6', markersize=markersize, linewidth=linewidth) + + ax.step(flop_cumsum_als, flops / ttime / mfps, + '-', label='ALS', color='C1', markersize=markersize, linewidth=linewidth) + + if ttb_l: + ax.step(flop_cumsum_als, flops / np.array(ttb_l) / mfps, + '-', label='TTB', color='C4', markersize=markersize, linewidth=linewidth) + + plot_gemm(gemm, ax, flop_cumsum_als) + + # Plot the CALS buffer size as xticks + # xticks = np.arange(1, cals_df['COLS'].count(), step=3) + # plt.xticks(ticks=xticks, labels=np.array(cals_df['COLS'])[xticks - 1], rotation=45, fontsize=3) + + # Plot the ALS ranks as xticks + # xticks = np.arange(1, len(ranks), step=1) + # plt.xticks(ticks=xticks, labels=ranks[xticks - 1], rotation=45, fontsize=3) + + # Plot total distance as xticks + flop_cumsum_cals = np.array(flop_cumsum_cals) + ax.set_xticks([0, 0.33 * flop_cumsum_cals[-1], 0.66 * flop_cumsum_cals[-1], flop_cumsum_cals[-1]]) + + if threads == '24' or print_all: + ax.set_xticklabels(['0', '.33', '.66', '1']) + # xticks = np.arange(1, len(ranks), step=1) + # plt.xticks(ticks=xticks, labels=ranks[xticks - 1], rotation=45, fontsize=3) + + # if (dic['modes'] == (200, 200, 200)) and (threads == '1'): + plot_x_ranks(ax, als_df) + + if ((dic['modes'] == (100, 100, 100) or dic['modes'] == (299, 301, 41)) and (threads == '12')) or print_all: + ax.legend() + + if ((dic['modes'] == (100, 100, 100)) or (dic['modes'] == (100, 100, 100) and threads == '1')) or print_all: + ax.set_ylabel('Efficiency (Threads: {})'.format(threads)) + else: + ax.tick_params(labelleft=False, left=True) + + if dic['modes'] == (299, 301, 41): + ax.set_ylabel('Efficiency (Threads: {})'.format(threads)) + ax.tick_params(labelleft=True, left=True) + else: + if threads == '24': + ax.set_xlabel('Total computation') + + # if threads_on_title: + # ax.set_title('Threads: {}'.format(threads)) + # else: + # ax.set_title(mode_string_title(dic['modes'])) + + if threads == "1" or print_all: + ax.set_title(modes_title_string(dic['modes'])) + + ax.set_xlim([-0.02 * flop_cumsum_cals[-1], flop_cumsum_cals[-1] + 0.02 * flop_cumsum_cals[-1]]) + + ax.set_ylim([0, 1]) + ax.set_yticks(ticks=np.arange(0, 1.1, step=0.1)) + + ax.grid(True, axis='y') + # plt.tight_layout() + if ax is None: + plt.savefig(plot_output_path + + 'ALS_v_CALS_' + + dic['backend'] + + '_modes_' + modes_string(dic['modes']) + + '_threads_' + str(dic['threads']) + + fig_format) + + +if __name__ == '__main__': + backend = 'MKL' + + fig, ax = plt.subplots(3, 3, sharey='all', sharex='col') + fig.set_size_inches(w=fig_size_in['width'], h=6) + for im, modes in enumerate([(100, 100, 100), (200, 200, 200), (300, 300, 300)]): + for it, threads in enumerate([1, 12, 24]): + results_dict = read_data(backend, threads, modes) + performance_plot_both(results_dict, ax[it][im]) + plt.tight_layout() + plt.subplots_adjust(wspace=0.11) + plt.savefig(plot_output_path + + 'ALS_v_CALS_' + + backend + + '_efficiency' + + fig_format) + plt.show() diff --git a/scripts/python/experiments/plot_figure_6.py b/scripts/python/experiments/plot_figure_6.py new file mode 100644 index 0000000..56b6cfe --- /dev/null +++ b/scripts/python/experiments/plot_figure_6.py @@ -0,0 +1,93 @@ +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from scripts.python.experiments.data_reader import extract_als_data, read_data +from scripts.python.utils.figure_config import fig_size_in, fig_format +from scripts.python.utils.system_config import plot_output_path +from scripts.python.utils.utils import modes_string, modes_title_string + +if __name__ == '__main__': + + modes_v = [] + modes_v.append((299, 301, 41)) + modes_v.append((405, 136, 19)) + modes_v.append((255, 281, 25)) + + for modes in modes_v: + backend = 'MKL' + + fig, ax = plt.subplots(1, 1) + fig.set_size_inches(w=fig_size_in['width'], h=3) + + index = [1, 24] + columns = {'TTB': [], 'CP-ALS': [], 'OMP ALS': [], 'CALS': []} + df = pd.DataFrame(index=index, columns=columns) + for th in index: + dic = read_data(backend, th, modes) + + als = dic['alsdata'] + als_cuda = dic['alscudadata'] + als_omp = dic['alsompdata'] + als_omp_cuda = dic['alsompcudadata'] + cals = dic['calsdata'] + cals_cuda = dic['calscudadata'] + ttb = dic['ttbdata'] + + df.at[th, 'TTB'] = np.sum(ttb) + + ttime, _, _, _, _ = extract_als_data(als) + df.at[th, 'CP-ALS'] = np.sum(ttime) + + tcals = cals['TOTAL'].max() + df.at[th, 'CALS'] = tcals + + + if th == 24: + ttime_omp, _, _, _, _ = extract_als_data(als_omp) + df.at[th, 'OMP ALS'] = np.max(ttime_omp) + + if th == 24 and isinstance(als_cuda, pd.DataFrame): + ttime_cu, _, _, _, _ = extract_als_data(als_cuda) + df.at['CUDA', 'CP-ALS'] = np.sum(ttime_cu) + + if th == 24 and isinstance(als_omp_cuda, pd.DataFrame): + ttime_omp_cu, _, _, _, _ = extract_als_data(als_omp_cuda) + df.at['CUDA', 'OMP ALS'] = np.max(ttime_omp_cu) + + if th == 24 and isinstance(cals_cuda, pd.DataFrame): + print('CUDA Iteration - CUDA Total: {:0.3f}'.format(cals_cuda['ITERATION'].sum() - cals_cuda['TOTAL'].max())) + df.at['CUDA', 'CALS'] = cals_cuda['ITERATION'].sum() + + index = ['1 thread', '24 threads', 'CUDA'] + df.index = index + + print(df.to_latex(float_format="{:0.2f}".format, na_rep='-')) + df.plot.bar(ax=ax, color=['C4', 'C1', 'C6', 'C0', 'C3'], rot=0) + ax.set_title(modes_title_string(modes)) + ax.set_ylabel('Time in seconds') + # ax.set_yscale('log') + # yticks = [1, 10, 100, 1000, 10000] + # ax.set_yticks(yticks) + # ax.set_yticklabels([str(i) for i in yticks]) + # ax.set_ylim([0.9, 110]) + + old_lim = list(ax.get_ylim()) + old_lim[1] += 0.05 * old_lim[1] + ax.set_ylim(old_lim) + for p in ax.patches: + height = int(round(p.get_height())) + if not p.get_height() == 0: + ax.annotate(str(height), + xy=(p.get_x() + p.get_width() / 2, height), + xytext=(0, 1), # 3 points vertical offset + textcoords="offset points", + ha='center', va='bottom') + plt.tight_layout() + plt.savefig(plot_output_path + + 'ALS_v_CALS_' + + backend + + '_real_' + + modes_string(modes) + + fig_format) + plt.show() diff --git a/scripts/python/experiments/plot_figure_7.py b/scripts/python/experiments/plot_figure_7.py new file mode 100644 index 0000000..6c036f6 --- /dev/null +++ b/scripts/python/experiments/plot_figure_7.py @@ -0,0 +1,61 @@ +import matplotlib.pyplot as plt +import pandas as pd + +from scripts.python.experiments.data_reader import read_data +from scripts.python.utils.figure_config import fig_size_in, fig_format +from scripts.python.utils.system_config import plot_output_path +from scripts.python.utils.utils import modes_title_string + +if __name__ == '__main__': + backend = 'MKL' + + fig, ax = plt.subplots(1, 1) + fig.set_size_inches(w=fig_size_in['width'], h=3) + modes_list = [(100, 100, 100), (200, 200, 200), (300, 300, 300)] + threads = [24] + # results = {'ALS': [], 'OMP ALS': [], 'CALS': [], 'ALSC': [], 'OMP ALSC': [], 'CALSC': []} + results = {'ALS CUDA': [], 'OMP ALS CUDA': [], 'CALS CUDA': []} + for modes in modes_list: + for th in threads: + dic = read_data(backend, th, modes) + als = dic['alsdata'] + alsomp = dic['alsompdata'] + alscuda = dic['alscudadata'] + alsompcuda = dic['alsompcudadata'] + cals = dic['calsdata'] + ttb = dic['ttbdata'] + cuda = dic['calscudadata'] + + # results['ALS'].extend([als['TOTAL'].sum()]) + # results['OMP ALS'].extend([alsomp['TOTAL'].max()]) + # results['CALS'].extend([cals['ITERATION'].sum()]) + + results['ALS CUDA'].extend([alscuda['TOTAL'].sum()]) + results['OMP ALS CUDA'].extend([alsompcuda['TOTAL'].max()]) + if th == 24 and isinstance(cuda, pd.DataFrame): + print('CUDA Iteration - CUDA Total: {:0.3f}'.format(cuda['ITERATION'].sum() - cuda['TOTAL'].max())) + results['CALS CUDA'].extend([cuda['TOTAL'].max()]) + + index = [modes_title_string(i) for i in modes_list] + df = pd.DataFrame(results, index=index) + # df.plot.bar(ax=ax, color=['C1', 'C6', 'C0', '#5fd35f', '#2ca02c', '#165016'], rot=0) + df.plot.bar(ax=ax, color=['#5fd35f', '#2ca02c', '#165016'], rot=0) + ax.set_ylabel('Time in seconds') + old_lim = list(ax.get_ylim()) + old_lim[1] += 0.05 * old_lim[1] + ax.set_ylim(old_lim) + for p in ax.patches: + height = round(p.get_height(), 1) + if not p.get_height() == 0: + ax.annotate(str(height), + xy=(p.get_x() + p.get_width() / 2, height), + xytext=(0, 1), # 3 points vertical offset + textcoords="offset points", + ha='center', va='bottom') + + plt.tight_layout() + plt.savefig(plot_output_path + + 'CUDA_v_CALS_' + + backend + + fig_format) + plt.show() diff --git a/scripts/python/experiments/plot_figure_letter_defrag.py b/scripts/python/experiments/plot_figure_letter_defrag.py new file mode 100644 index 0000000..1292084 --- /dev/null +++ b/scripts/python/experiments/plot_figure_letter_defrag.py @@ -0,0 +1,28 @@ +import logging + +from scripts.python.experiments.data_reader import read_data + +logger = logging.getLogger('Letter defrag') + +logging.basicConfig(level=logging.INFO) + + +def defrag_plot(backend, threads, modes): + dic = read_data(backend, threads, modes, 'defrag') + + cals = dic['calsdata'] + + cals['P'] = cals['DEFRAGMENTATION'] / cals['ITERATION'] + logger.info("Percent mean: {}".format(cals['P'].mean())) + logger.info("Percent median: {}".format(cals['P'].median())) + logger.info("Percent max: {}".format(cals['P'].max())) + logger.info("Percent min: {}".format(cals['P'].min())) + + +if __name__ == '__main__': + + backend = 'MKL' + + for im, modes in enumerate([(200, 200, 200)]): + for i, threads in enumerate([1, 24]): + defrag_plot(backend, threads, modes) diff --git a/scripts/python/experiments/utils.py b/scripts/python/experiments/utils.py new file mode 100644 index 0000000..0f2b2b4 --- /dev/null +++ b/scripts/python/experiments/utils.py @@ -0,0 +1,29 @@ +import numpy as np +import pandas as pd + +from scripts.python.experiments.data_reader import extract_als_data + + +def speedup(dic): + als = dic['alsdata'] + cals = dic['calsdata'] + ccals = dic['ccalsdata'] + + tcals = cals['TOTAL'].max() + icals = cals['ITERATION'].sum() + if isinstance(ccals, pd.DataFrame): + tccals = cals['TOTAL'].max() + iccals = cals['ITERATION'].sum() + + ttime, ctime, flops, ranks = extract_als_data(als) + tals = np.sum(ttime) + + print('ALS time: {}'.format(tals)) + print('CALS time: It: {} To:{}'.format(icals, tcals)) + print('ALS v CALS(t) Speedup: ', '{:.2f}'.format(tals / tcals)) + print('ALS v CALS(i) Speedup: ', '{:.2f}'.format(tals / icals)) + + if isinstance(ccals, pd.DataFrame): + print('CUDA time: It: {} To:{}'.format(iccals, tccals)) + print('CALS v CUDA(t) Speedup: ', '{:.2f}'.format(tcals / tccals)) + print('CALS v CUDA(i) Speedup: ', '{:.2f}'.format(icals / iccals)) diff --git a/scripts/python/mttkrp/__init__.py b/scripts/python/mttkrp/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scripts/python/mttkrp/cals_mttkrp_analysis.py b/scripts/python/mttkrp/cals_mttkrp_analysis.py new file mode 100644 index 0000000..f9742ec --- /dev/null +++ b/scripts/python/mttkrp/cals_mttkrp_analysis.py @@ -0,0 +1,205 @@ +import logging + +import matplotlib.pyplot as plt +import numpy as np + +from scripts.python.mttkrp.data_reader import read_bench +from scripts.python.mttkrp.generate_LUTs import best_method_per_rank +from scripts.python.utils.figure_config import markersize, linewidth, fig_format, fig_size_in +from scripts.python.utils.system_config import CPU_FPS, GPU_FPS, plot_output_path +from scripts.python.utils.utils import modes_string, modes_title_string, MttkrpMethod + +logger = logging.getLogger('CALS MTTKRP Analyzer') + + +def mttkrp_performance_analysis(backend, modes, threads, gpu=False): + fig, ax = plt.subplots(len(modes), 1, sharex='all') + fig.set_size_inches(w=fig_size_in['width'], h=5) + + if gpu: + mfps = GPU_FPS + else: + mfps = CPU_FPS[str(threads)] + mttkrp_configurations = [MttkrpMethod.MTTKRP, MttkrpMethod.TWOSTEP0, MttkrpMethod.TWOSTEP1] + + for midx, mode in enumerate(modes): + for m in mttkrp_configurations: + dic = read_bench(backend, threads, modes, m, gpu) + df = dic['data'] + dm = df.loc[df['MODE'] == midx] + y = dm['FLOPS'] / dm['TIME'] / mfps + label, color = get_label_and_color(dic['mttkrp_method'], midx, modes) + ax[midx].plot(dm['RANK'], y, '-o', label=label, c=color, markersize=markersize, linewidth=linewidth) + # ax[midx].set_xticks(np.arange(dm['RANK'].min(), dm['RANK'].max(), step=49)) + ax[midx].set_ylim([0, 1]) + ax[midx].set_yticks(np.arange(0, 1.1, step=0.2)) + ax[midx].set_ylabel('Efficiency') + ax[midx].grid(True) + ax[midx].set_xscale('log') + ax[midx].set_title('Mode {}'.format(midx)) + ax[midx].tick_params(labelright=True) + ax[midx].legend(loc='upper left') + ax[-1].set_xlabel('Components') + ax[-1].set_xticks([1, 10, 100, 1000]) + ax[-1].set_xticklabels(['1', '10', '100', '1000']) + fig.suptitle('CALS Implementations (BLAS: ' + backend + + ', Threads: ' + str(threads) + + ', Modes: ' + modes_title_string(modes) + ')') + + plt.tight_layout() + fig.savefig(plot_output_path + + 'MTTKRP_Perf_' + backend + + '_threads_' + str(threads) + + '_modes_' + modes_string(modes) + + fig_format) + return fig + + +def get_label_and_color(method, mode_index, modes): + simple = True + + I = modes[0] + J = modes[1] + K = modes[2] + label, color = '', '' + if method == MttkrpMethod.MTTKRP: + if simple: + label = 'KRP + MTTKRP' + else: + if mode_index == 0: + label = '({} x {}) * ({} x R) - {} block'.format(I, J * K, J * K, 1) + elif mode_index == 1: + label = '({} x {}) * ({} x R) - {} blocks'.format(J, I, I, K) + elif mode_index == 2: + label = '({} x {}) * ({} x R) - {} block'.format(K, I * J, I * J, 1) + else: + logger.error('midx exceeds 3D size ({}). This analyzer is designed for 3D tensors.'.format(mode_index)) + exit() + color = 'r' + elif method == MttkrpMethod.TWOSTEP0: + if simple: + label = 'TTM + TTV' + else: + if mode_index == 0: + label = '({} x {}) * ({} x R) - {} block'.format(I * J, K, K, 1) + elif mode_index == 1: + label = '({} x {}) * ({} x R) - {} block'.format(I * J, K, K, 1) + elif mode_index == 2: + label = '({} x {}) * ({} x R) - {} block'.format(I * K, J, J, 1) + else: + logger.error('midx exceeds 3D size ({}). This analyzer is designed for 3D tensors.'.format(mode_index)) + exit() + color = 'b' + elif method == MttkrpMethod.TWOSTEP1: + if simple: + label = 'TTM + TTV' + else: + if mode_index == 0: + label = '({} x {}) * ({} x R) - {} blocks'.format(I * K, J, J, K) + elif mode_index == 1: + label = '({} x {}) * ({} x R) - {} block'.format(J * K, I, I, 1) + elif mode_index == 2: + label = '({} x {}) * ({} x R) - {} blocks'.format(J * K, I, I, K) + else: + logger.error('midx exceeds 3D size ({}). This analyzer is designed for 3D tensors.'.format(mode_index)) + exit() + color = 'g' + else: + logger.error('MTTKRP method not found: {}'.format(method)) + exit() + return label, color + + +def plot_best_mttkrp(backend, modes, threads, best, ax=None, c=None, f_ylabel=True, f_xlabel=True, f_legend=True, gpu=False): + + mfps = CPU_FPS[str(threads)] + if gpu: + mfps = 7e12 + + # Accumulate the FLOPS and the TIME per mode, in order to calculate the efficiency of MTTKRP for all modes + x = np.array(0) + y_time = np.array(0) + y_flops = np.array(0) + for idx, data in enumerate(best): + if idx == 0: + x = np.array([rank for rank, flops, time, method in data]) + y_time = np.array([time for rank, flops, time, method in data]) + y_flops = np.array([flops for rank, flops, time, method in data]) + else: + y_time += np.array([time for rank, flops, time, method in data]) + y_flops += np.array([flops for rank, flops, time, method in data]) + y = y_flops / y_time / mfps + + # Remove rank values from 11 to 19 (to conform to log scale) + x, y = list(x), list(y) + for rank in range(11, 20): + if rank in x: + ind = x.index(rank) + x.pop(ind) + y.pop(ind) + x, y = np.array(x), np.array(y) + + if threads == 1: + label = '1 thread' + else: + label = '{} threads'.format(threads) + + fig = None + if ax is None: + fig, ax = plt.subplots(1, 1) + ax.plot(x, y, '-o', color=c, label=label, markersize=markersize, linewidth=linewidth) + + ################### + # Figure formatting + ################### + + # y axis + ax.set_ylim([0, 1]) + ax.set_yticks(np.arange(0, 1.1, step=0.2)) + if f_ylabel: + ax.set_ylabel('Efficiency') + + # x axis + # if modes == (300, 300, 300) or modes == (299, 301, 41): + ax.set_xscale('log') + ax.set_xticks([1, 10, 100, 1000]) + + if f_xlabel: + ax.set_xticklabels([str(i) for i in [1, 10, 100, 1000]]) + ax.set_xlabel('Components') + + # rest + ax.tick_params(labelleft=True, left=True) + ax.grid(b=True, axis='y') + + if f_legend: + ax.legend(loc='upper left') + + ax.set_title(modes_title_string(modes)) + if fig: + fig.savefig(plot_output_path + + 'MTTKRP_Best_Benchmark_' + + backend + + '_threads_' + str(threads) + + '_modes_' + modes_string(modes) + + fig_format) + return ax + + +if __name__ == '__main__': + + backend = 'MKL' + gpu = False + modes_l = [] + # modes_l.append((117, 18, 702)) + # modes_l.append((299, 301, 41)) + # modes_l.append((405, 136, 19)) + # modes_l.append((100, 100, 100)) + modes_l.append((200, 200, 200)) + # modes_l.append((300, 300, 300)) + for modes in modes_l: + for threads in [1, 24]: + mttkrp_performance_analysis(backend, modes, threads, gpu) + best = best_method_per_rank(backend, modes, threads, gpu) + plot_best_mttkrp(backend, modes, threads, best, gpu=gpu) + plt.show() diff --git a/scripts/python/mttkrp/data_reader.py b/scripts/python/mttkrp/data_reader.py new file mode 100644 index 0000000..d72dea8 --- /dev/null +++ b/scripts/python/mttkrp/data_reader.py @@ -0,0 +1,30 @@ +import logging +import os.path + +import pandas as pd + +from scripts.python.utils.system_config import input_path +from scripts.python.utils.utils import modes_string + +logger = logging.getLogger('Reader') + + +def read_bench(backend, threads, modes, mttkrp_method, gpu=False): + bench_dict = {} + folder = input_path + '{}/benchmark/'.format(backend) + mode_name = modes_string(modes) + if gpu: + cuda = "_CUDA" + else: + cuda = "" + bench_file_name = '{}benchmark{}_{}_{}_{}_{}.csv'.format(folder, cuda, backend, mode_name, threads, mttkrp_method) + if os.path.isfile(bench_file_name): + logger.info('Found file: {}'.format(bench_file_name)) + bench_dict.update({'backend': backend, + 'modes': modes, + 'threads': threads, + 'mttkrp_method': mttkrp_method, + 'data': pd.read_csv(bench_file_name, delimiter=';')}) + else: + logger.warning('File: {} Not Found!'.format(bench_file_name)) + return bench_dict diff --git a/scripts/python/mttkrp/generate_LUTs.py b/scripts/python/mttkrp/generate_LUTs.py new file mode 100644 index 0000000..d116e75 --- /dev/null +++ b/scripts/python/mttkrp/generate_LUTs.py @@ -0,0 +1,77 @@ +import logging + +logging.basicConfig(level=logging.INFO) +from pathlib import Path + +from scripts.python.mttkrp.data_reader import read_bench +from scripts.python.utils.system_config import lut_output_path +from scripts.python.utils.utils import MttkrpMethod, modes_string + +logger = logging.getLogger('LUT generator') + + +def best_method_per_rank(backend, modes, threads, gpu=False, export=False): + mttkrp_methods = [MttkrpMethod.MTTKRP, MttkrpMethod.TWOSTEP0, MttkrpMethod.TWOSTEP1] + best = [] + + for method in mttkrp_methods: + bench_dict = read_bench(backend, threads, modes, method, gpu) + get_best_method(best, bench_dict) + + if export: + export_to_file(backend, modes, threads, best, gpu) + + return best + + +def get_best_method(best, bench_dict): + method = bench_dict['mttkrp_method'] + for midx, mode in enumerate(bench_dict['modes']): + + df = bench_dict['data'] + dm = df.loc[df['MODE'] == midx] + if len(best) < midx + 1: + best.append([(e['RANK'], e['FLOPS'], e['TIME'], int(method)) for i, e in dm.iterrows()]) + else: + for i, e in dm.reset_index().iterrows(): + (rank, best_flops, best_time, best_method) = best[midx][i] + if not rank == e['RANK']: + logger.error('Rank misalignment. Benchmarks were not performed for the same ranks.') + exit() + if best_flops / best_time < e['FLOPS'] / e['TIME']: + best[midx][i] = (e['RANK'], e['FLOPS'], e['TIME'], int(method)) + + +def export_to_file(backend, modes, threads, best, gpu): + if gpu: + path_to_files = lut_output_path + "GPU" + '/lookup_tables/' + modes_string(modes) + else: + path_to_files = lut_output_path + backend + '/lookup_tables/' + modes_string(modes) + '/' + str(threads) + logger.info('Path to files: {}'.format(path_to_files)) + + Path(path_to_files).mkdir(parents=True, exist_ok=True) + + for idx, mode in enumerate(modes): + f = open(path_to_files + '/' + str(idx), "w") + for (rank, flops, time, method) in best[idx]: + f.write('{} {}\n'.format(rank, method)) + + +if __name__ == '__main__': + + backend = 'MKL' + gpu = True + modes_l = [] + # modes_l.append((117, 18, 702)) + # modes_l.append((299, 301, 41)) + # modes_l.append((405, 136, 19)) + modes_l.append((255, 281, 25)) + # modes_l.append((100, 100, 100)) + # modes_l.append((200, 200, 200)) + # modes_l.append((300, 300, 300)) + for modes in modes_l: + for threads in [1, 24]: + if not gpu: + best_method_per_rank(backend, modes, threads, gpu=gpu, export=True) + elif threads == 24: # make sure that if gpu is enabled, only the 24 thread case is used. + best_method_per_rank(backend, modes, threads, gpu=gpu, export=True) diff --git a/scripts/python/mttkrp/mttkrp_implementations_analysis.py b/scripts/python/mttkrp/mttkrp_implementations_analysis.py new file mode 100644 index 0000000..265c627 --- /dev/null +++ b/scripts/python/mttkrp/mttkrp_implementations_analysis.py @@ -0,0 +1,161 @@ +import logging + +import matplotlib.pyplot as plt +import numpy as np + +from scripts.python.mttkrp.data_reader import read_bench +from scripts.python.utils.figure_config import markersize, linewidth, fig_format, fig_size_in +from scripts.python.utils.system_config import CPU_FPS, plot_output_path +from scripts.python.utils.utils import modes_string, modes_title_string, MttkrpMethod + +logger = logging.getLogger('CALS MTTKRP Implementation Analyzer') + + +def mttkrp_performance_analysis(backend, modes, threads, methods): + fig, ax = plt.subplots(len(modes), 1, sharex='all') + fig.set_size_inches(w=fig_size_in['width'], h=5) + + mfps = CPU_FPS[str(threads)] + + flops = [] + for midx, mode in enumerate(modes): + for m in methods: + dic = read_bench(backend, threads, modes, m) + df = dic['data'] + dm = df.loc[df['MODE'] == midx] + if m == MttkrpMethod.AUTO: + flops = dm['FLOPS'] + if m == MttkrpMethod.CTF or m == MttkrpMethod.PLANC: + y = flops / dm['TIME'] / mfps + else: + y = dm['FLOPS'] / dm['TIME'] / mfps + label, color = get_label_and_color(dic['mttkrp_method']) + ax[midx].plot(dm['RANK'], y, '-o', label=label, c=color, markersize=markersize, linewidth=linewidth) + # ax[midx].set_xticks(np.arange(dm['RANK'].min(), dm['RANK'].max(), step=49)) + ax[midx].set_ylim([0, 1]) + ax[midx].set_yticks(np.arange(0, 1.1, step=0.2)) + ax[midx].set_ylabel('Efficiency') + ax[midx].grid(True) + ax[midx].set_xscale('log') + ax[midx].set_title('Mode {}'.format(midx)) + ax[midx].tick_params(labelright=True) + ax[midx].legend(loc='upper left') + ax[-1].set_xlabel('Components') + ax[-1].set_xticks([1, 10, 100, 1000]) + ax[-1].set_xticklabels(['1', '10', '100', '1000']) + fig.suptitle('Comparison (BLAS: ' + backend + + ', Threads: ' + str(threads) + + ', Modes: ' + modes_title_string(modes) + ')') + + plt.tight_layout() + fig.savefig(plot_output_path + + 'MTTKRP_Comp_' + backend + + '_threads_' + str(threads) + + '_modes_' + modes_string(modes) + + fig_format) + return fig + + +def get_label_and_color(method): + label, color = '', '' + if method == MttkrpMethod.AUTO: + label = "CALS MTTKRP" + color = 'r' + elif method == MttkrpMethod.CTF: + label = "CTF" + color = 'g' + elif method == MttkrpMethod.PLANC: + label = "PLANC" + color = 'b' + else: + logger.error('MTTKRP method not found: {}'.format(method)) + exit() + + return label, color + + +def plot_best_mttkrp(backend, modes, threads, best, ax=None, c=None, f_ylabel=True, f_xlabel=True, f_legend=True): + mfps = CPU_FPS[str(threads)] + + # Accumulate the FLOPS and the TIME per mode, in order to calculate the efficiency of MTTKRP for all modes + x = np.array(0) + y_time = np.array(0) + y_flops = np.array(0) + for idx, data in enumerate(best): + if idx == 0: + x = np.array([rank for rank, flops, time, method in data]) + y_time = np.array([time for rank, flops, time, method in data]) + y_flops = np.array([flops for rank, flops, time, method in data]) + else: + y_time += np.array([time for rank, flops, time, method in data]) + y_flops += np.array([flops for rank, flops, time, method in data]) + y = y_flops / y_time / mfps + + # Remove rank values from 11 to 19 (to conform to log scale) + x, y = list(x), list(y) + for rank in range(11, 20): + if rank in x: + ind = x.index(rank) + x.pop(ind) + y.pop(ind) + x, y = np.array(x), np.array(y) + + if threads == 1: + label = '1 thread' + else: + label = '{} threads'.format(threads) + + fig = None + if ax is None: + fig, ax = plt.subplots(1, 1) + ax.plot(x, y, '-o', color=c, label=label, markersize=markersize, linewidth=linewidth) + + ################### + # Figure formatting + ################### + + # y axis + ax.set_ylim([0, 1]) + ax.set_yticks(np.arange(0, 1.1, step=0.2)) + if f_ylabel: + ax.set_ylabel('Efficiency') + + # x axis + # if modes == (300, 300, 300) or modes == (299, 301, 41): + ax.set_xscale('log') + ax.set_xticks([1, 10, 100, 1000]) + + if f_xlabel: + ax.set_xticklabels([str(i) for i in [1, 10, 100, 1000]]) + ax.set_xlabel('Rank') + + # rest + ax.tick_params(labelleft=True, left=True) + ax.grid(b=True, axis='y') + + if f_legend: + ax.legend(loc='upper left') + + ax.set_title(modes_title_string(modes)) + if fig: + fig.savefig(plot_output_path + + 'MTTKRP_Best_Benchmark_' + + backend + + '_threads_' + str(threads) + + '_modes_' + modes_string(modes) + + fig_format) + return ax + + +def main(): + backend = 'MKL' + methods = [MttkrpMethod.AUTO, MttkrpMethod.CTF, MttkrpMethod.PLANC] + for modes in [(200, 200, 200)]: + for threads in [1, 24]: + mttkrp_performance_analysis(backend, modes, threads, methods) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/scripts/python/utils/__init__.py b/scripts/python/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scripts/python/utils/figure_config.py b/scripts/python/utils/figure_config.py new file mode 100644 index 0000000..8b9ced6 --- /dev/null +++ b/scripts/python/utils/figure_config.py @@ -0,0 +1,25 @@ +import matplotlib +import matplotlib.pyplot as plt +plt.switch_backend('Qt5Agg') + +# General configurations +linewidth = 1 +markersize = 2 +colors = ['C0', 'C1', 'C3'] + +fig_size_in = {'width': 5.47807, 'height': 6} +fig_format = '.png' + +# f_latex = True +f_latex = False +if f_latex: + matplotlib.use("pgf") + matplotlib.rcParams.update({'text.usetex': True}) + matplotlib.rcParams.update({'pgf.rcfonts': False}) + matplotlib.rcParams.update({'font.family': 'serif'}) + matplotlib.rcParams.update({'pgf.texsystem': 'xelatex'}) + # matplotlib.rcParams.update({'font.size': 22}) + # matplotlib.rcParams.update({'figure.dpi': 300}) + + fig_size_in = {'width': 5.47807, 'height': 3} + fig_format = '.pgf' \ No newline at end of file diff --git a/scripts/python/utils/system_config.py b/scripts/python/utils/system_config.py new file mode 100644 index 0000000..ab052ef --- /dev/null +++ b/scripts/python/utils/system_config.py @@ -0,0 +1,58 @@ +import copy +import logging + +logger = logging.getLogger('config') + +input_path = 'data/' +lut_output_path = input_path +plot_output_path = 'data/figures/' + + +def generate_flops_per_sec(flops_per_cycle, cpu_ghz): + flops_per_sec = copy.deepcopy(cpu_ghz) + for cores, ghz in flops_per_sec.items(): + flops_per_sec[cores] = ghz * 1e9 * flops_per_cycle * int(cores) + return flops_per_sec + + +arch = 'skx' + +if arch == 'skx': + + # CLAIX18 (Turbo - SKYLAKE (2x) Xeon Platinum 8160) + CPU_GHZ = {'1': 3.5, '12': 2.6, '24': 2} + CPU_FPC = 32 + CPU_FPS = generate_flops_per_sec(CPU_FPC, CPU_GHZ) + GPU_FPS = 7e12 # V100 + + # TODO: Have the script auto generate tables with this data instead of manually extracting it. + # Statistics for GEMMs of 3 matrices, whose elements sum up to the elements of a tensor (absolute best). + GEMM = {'100-100-100': {'1': {'mean': 0.78456, 'median': 0.81856, 'std': 0.0895013}, + '12': {'mean': 0.676944, 'median': 0.687896, 'std': 0.0320906}, + '24': {'mean': 0.554468, 'median': 0.566198, 'std': 0.062533}}, + '200-200-200': {'1': {'mean': 0.857106, 'median': 0.892538, 'std': 0.0810871}, + '12': {'mean': 0.748761, 'median': 0.748748, 'std': 0.0593422}, + '24': {'mean': 0.744228, 'median': 0.745608, 'std': 0.0111339}}, + '300-300-300': {'1': {'mean': 0.863431, 'median': 0.894378, 'std': 0.0775792}, + '12': {'mean': 0.857263, 'median': 0.883854, 'std': 0.0517553}, + '24': {'mean': 0.833132, 'median': 0.834759, 'std': 0.0134416}}, + '299-301-41': {'1': {'mean': 0.857106, 'median': 0.892538, 'std': 0.0810871}, + '12': {'mean': 0.748761, 'median': 0.748748, 'std': 0.0593422}, + '24': {'mean': 0.744228, 'median': 0.745608, 'std': 0.0111339}}} +elif arch == 'haswell': + + # Node 72 (No Turbo - HASWELL (2x) E5-2680 v3) + CPU_GHZ = {'1': 2.5, '12': 2.5, '24': 2.5} + CPU_FPC = 16 + CPU_FPS = generate_flops_per_sec(CPU_FPC, CPU_GHZ) + +elif arch == 'sandy': + + # Rubik (No Turbo - SANDYBRIDGE (2x) E5-2670 v2) + CPU_GHZ = {'1': 2.5, '10': 2.5, '20': 2.5} + CPU_FPC = 8 + CPU_FPS = generate_flops_per_sec(CPU_FPC, CPU_GHZ) + +else: + logger.error('Architecture {} not supported.'.format(arch)) + exit() diff --git a/scripts/python/utils/utils.py b/scripts/python/utils/utils.py new file mode 100644 index 0000000..1162535 --- /dev/null +++ b/scripts/python/utils/utils.py @@ -0,0 +1,42 @@ +import copy +from enum import IntEnum + +from scripts.python.utils.figure_config import markersize, linewidth, f_latex + + +class MttkrpMethod(IntEnum): + MTTKRP = 0 + TWOSTEP0 = 1 + TWOSTEP1 = 2 + AUTO = 3 + CTF = 4 + PLANC = 5 + + +def modes_string(modes, symbol='-'): + mode_name = "" + for i, m in enumerate(modes): + if i != len(modes) - 1: + mode_name += str(m) + symbol + else: + mode_name += str(m) + return mode_name + + +def modes_title_string(modes): + if f_latex: + return modes_string(modes, symbol='$\\times$') + else: + return modes_string(modes, symbol='x') + + +def plot_gemm(gemm, ax, x, color='C3'): + # Adjust x to start from little bit left and right of actual values of x + x_copy = copy.deepcopy(x) + x_copy[0] -= 0.1 * x_copy[-1] + x_copy[-1] += 0.1 * x_copy[-1] + + # Plot gemm + ax.axhline(gemm['mean'], ls='-', color=color, markersize=markersize, linewidth=linewidth, label='GEMM') + ax.axhline(gemm['median'], ls='--', color=color, markersize=markersize, linewidth=linewidth) + ax.fill_between(x_copy, gemm['mean'] - gemm['std'], gemm['mean'] + gemm['std'], alpha=0.2, color=color) diff --git a/scripts/slurm/CLAIX_18_cals.sl b/scripts/slurm/CLAIX_18_cals.sl index 2fdb1a6..bcdaf36 100755 --- a/scripts/slurm/CLAIX_18_cals.sl +++ b/scripts/slurm/CLAIX_18_cals.sl @@ -1,28 +1,27 @@ #!/usr/local_rwth/bin/zsh -#SBATCH --job-name=CALS +#SBATCH --job-name=CALS-EXP #SBATCH --nodes=1 #SBATCH --ntasks=1 -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=1 -#SBATCH --mem-per-cpu=1G -#SBATCH --output=CALS-%j.out -#SBATCH --error=CALS-%j.err -#SBATCH --time=10:00:00 +#SBATCH --cpus-per-task=48 +#SBATCH --mem=40G +#SBATCH --output=output.%J.txt +#SBATCH --time=05:00:00 #SBATCH --partition=c18m -#SBATCH --account=rwth0575 #SBATCH --exclusive -module purge -module load DEVELOP -module load gcc/8 -module load LIBRARIES -module load intelmkl/2019 -module load cmake/3.13.2 +# if NOT CTF +#module load clang/9.0 +# else +module load gcc/9 +#module switch intelmpi/2018 openmpi/4.0.3 +# fi + +#module load LIBRARIES +#module load intelmkl/2020 source ~/.zshrc.local cd ${CALS_DIR} || exit -export GOMP_CPU_AFFINITY="0 1 2 6 7 8 12 13 14 18 19 20 3 4 5 9 10 11 15 16 17 21 22 23" # CLAIX18 xeon platinum 8160 if [ -d "build" ]; then rm -rvf build/* @@ -34,16 +33,34 @@ else fi cd build || exit -cmake -DCMAKE_BUILD_TYPE=Release -DWITH_TESTS=Off -DWITH_MKL=On -DWITH_OPENBLAS=Off -DWITH_BLIS=Off -DWITH_CUBLAS=Off .. +#CC=clang CXX=clang++ ./../extern/cmake/bin/cmake -DCMAKE_BUILD_TYPE=Release -DWITH_MKL=On -DWITH_EXPERIMENTS=Off -DWITH_DIAGNOSTICS=1 .. +CC=gcc CXX=g++ ./../extern/cmake/bin/cmake -DCMAKE_BUILD_TYPE=Release -DWITH_MKL=On -DWITH_EXPERIMENTS=Off -DWITH_DIAGNOSTICS=1 .. make -j 48 -./Experiment_MKL 1 -./Experiment_MKL 12 -./Experiment_MKL 24 -#./Experiment_MKL 48 -#./Experiment_OPENBLAS 1 -#./Experiment_OPENBLAS 12 +numactl -H +lscpu +#export GOMP_CPU_AFFINITY="0 1 2 6 7 8 12 13 14 18 19 20 3 4 5 9 10 11 15 16 17 21 22 23" # CLAIX18 xeon platinum 8160 + +numactl --cpubind=0,1 --membind=0,1 -- numactl -show +export OMP_NUM_THREADS=24 +export OMP_THREAD_LIMIT=24 +numactl --cpubind=0,1 --membind=0,1 -- ./src/experiments/experiments_MKL 24 +#numactl --cpubind=0,1 --membind=0,1 -- ./src/experiments/benchmark_cals_mttkrp_MKL 24 +#numactl --cpubind=0,1 --membind=0,1 -- ./src/experiments/benchmark_other_mttkrp_MKL 24 + +#numactl --cpubind=0 --membind=0 -- numactl -show +#export OMP_NUM_THREADS=12 +#export OMP_THREAD_LIMIT=12 +#numactl --cpubind=0 --membind=0 -- ./src/experiments/experiments_MKL 12 +#numactl --cpubind=0 --membind=0 -- ./src/experiments/benchmark_cals_mttkrp_MKL 12 +#numactl --cpubind=0 --membind=0 -- ./src/experiments/benchmark_other_mttkrp_MKL 12 + + +numactl --cpubind=0 --membind=0 -- numactl -show +export OMP_NUM_THREADS=1 +export OMP_THREAD_LIMIT=1 +numactl --cpubind=0 --membind=0 -- ./src/experiments/experiments_MKL 1 +#numactl --cpubind=0 --membind=0 -- ./src/experiments/benchmark_cals_mttkrp_MKL 1 +#numactl --cpubind=0 --membind=0 -- ./src/experiments/benchmark_other_mttkrp_MKL 1 -#./Experiment_BLIS 1 -#./Experiment_BLIS 12 -make clean +# make clean diff --git a/scripts/slurm/CLAIX_18_cuda.sl b/scripts/slurm/CLAIX_18_cuda.sl index acbf988..214cec1 100755 --- a/scripts/slurm/CLAIX_18_cuda.sl +++ b/scripts/slurm/CLAIX_18_cuda.sl @@ -1,30 +1,24 @@ #!/usr/local_rwth/bin/zsh -#SBATCH --job-name=CALS-CUDA +#SBATCH --job-name=Bench-CU #SBATCH --nodes=1 #SBATCH --ntasks=1 +#SBATCH --cpus-per-task=1 #SBATCH --mem=50G -#SBATCH --output=cuda-%j.out -#SBATCH --error=cuda-%j.err -#SBATCH --time=01:00:00 +#SBATCH --output=output.%J.txt +#SBATCH --time=02:00:00 #SBATCH --partition=c18g #SBATCH --gres=gpu:volta:2 -#SBATCH --account=rwth0575 #SBATCH --exclusive +# comment -B 1:1 -module purge module load DEVELOP -module load gcc/8 -module load LIBRARIES -module load intelmkl/2019 -module load cmake/3.13.2 -module load cuda/102 +module load gcc/9 +module load cuda/110 source ~/.zshrc.local cd ${CALS_DIR} || exit -export GOMP_CPU_AFFINITY="0 1 2 6 7 8 12 13 14 18 19 20 3 4 5 9 10 11 15 16 17 21 22 23" # CLAIX18 xeon platinum 8160 - if [ -d "build_cuda" ]; then rm -rvf build_cuda/* rm -rv build_cuda @@ -34,9 +28,15 @@ else fi cd build_cuda || exit -cmake -DCMAKE_BUILD_TYPE=Release -DWITH_TESTS=Off -DWITH_MKL=On -DWITH_OPENBLAS=Off -DWITH_BLIS=Off -DWITH_CUBLAS=On .. -make -j 24 CUDA_Experiment_MKL - -./CUDA_Experiment_MKL 24 +#CC=clang CXX=clang++ ./../extern/cmake/bin/cmake -DCMAKE_BUILD_TYPE=Release -DWITH_MKL=On -DWITH_EXPERIMENTS=Off -DWITH_DIAGNOSTICS=2 -DWITH_CUBLAS=On .. +CC=gcc CXX=g++ ./../extern/cmake/bin/cmake -DCMAKE_BUILD_TYPE=Release -DWITH_MKL=On -DWITH_EXPERIMENTS=Off -DWITH_DIAGNOSTICS=2 -DWITH_CUBLAS=On .. +make -j 48 + +numactl -H +numactl --cpubind=0,1 --membind=0,1 -- numactl -show +export OMP_NUM_THREADS=24 +export OMP_THREAD_LIMIT=24 +numactl --cpubind=0,1 --membind=0,1 -- ./src/experiments/experiments_MKL 24 +#numactl --cpubind=0 --membind=0 -- ./src/experiments/benchmark_cals_mttkrp_MKL 24 # make clean diff --git a/scripts/slurm/CLAIX_18_matlab.sl b/scripts/slurm/CLAIX_18_matlab.sl index 7646fa2..467a081 100755 --- a/scripts/slurm/CLAIX_18_matlab.sl +++ b/scripts/slurm/CLAIX_18_matlab.sl @@ -8,9 +8,8 @@ #SBATCH --mem-per-cpu=1G #SBATCH --output=cals-matlab-%j.out #SBATCH --error=cals-matlab-%j.err -#SBATCH --time=2:00:00 +#SBATCH --time=8:00:00 #SBATCH --partition=c18m -#SBATCH --account=rwth0575 #SBATCH --exclusive module purge @@ -35,7 +34,7 @@ source ~/.zshrc.local exppath="${CALS_DIR}/matlab/matlab_src" -export GOMP_CPU_AFFINITY="0 1 2 6 7 8 12 13 14 18 19 20 3 4 5 9 10 11 15 16 17 21 22 23" # CLAIX18 xeon platinum 8160 +#export GOMP_CPU_AFFINITY="0 1 2 6 7 8 12 13 14 18 19 20 3 4 5 9 10 11 15 16 17 21 22 23" # CLAIX18 xeon platinum 8160 export MATLAB_LOG_DIR=. export MATLABPATH=$exppath @@ -46,20 +45,22 @@ export MKL_NUM_THREADS=24 export OMP_NUM_THREADS=24 matlab -nodisplay -nodesktop -nosplash -logfile /dev/null < #include "als.h" -#include - -#include "utils.h" +#include "utils/error.h" +#include "utils/line_search.h" #if CUDA_ENABLED @@ -49,8 +12,9 @@ using std::cout; using std::endl; -using ALS_TIMERS = cals::AlsTimer::ALS_TIMERS; -using MODE_TIMERS = cals::ModeTimer::MODE_TIMERS; +using ALS_TIMERS = cals::AlsTimers::TIMERS; +using MODE_TIMERS = cals::ModeTimers::TIMERS; +using MTTKRP_TIMERS = cals::MttkrpTimers::TIMERS; namespace cals { @@ -62,7 +26,7 @@ namespace cals cout << "Tensor size: [ "; for (auto mode : X.get_modes()) cout << mode << " "; cout << "]" << endl; - cout << "Ktensor rank= " << ktensor.get_rank() << endl; + cout << "Ktensor rank= " << ktensor.get_components() << endl; ) AlsReport rep; @@ -76,7 +40,7 @@ namespace cals rep.max_iter = params.max_iterations; rep.n_threads = get_threads(); rep.ktensor_id = ktensor.get_id(); - rep.ktensor_rank = ktensor.get_rank(); + rep.ktensor_rank = ktensor.get_components(); rep.tol = params.tol; rep.cuda = params.cuda; rep.update_method = params.update_method; @@ -84,14 +48,28 @@ namespace cals rep.line_search_interval = params.line_search_interval; rep.line_search_step = params.line_search_step; - TIME(rep.init_timers();) - TIME(rep.als_timer.timers[cals::AlsTimer::TOTAL].start();) +#if WITH_TIME + rep.als_times = Matrix(ALS_TIMERS::LENGTH, rep.max_iter); + rep.mode_times = Matrix(MODE_TIMERS::LENGTH * rep.n_modes, rep.max_iter); + rep.mttkrp_times = Matrix(MTTKRP_TIMERS::LENGTH * rep.n_modes, rep.max_iter); + auto &als_times = rep.als_times; + auto &mode_times = rep.mode_times; + auto &mttkrp_times = rep.mttkrp_times; +#endif + + Timer total_time; + AlsTimers als_timers; + ModeTimers mode_timers; + + using ALS_TIMERS = cals::AlsTimers::TIMERS; + + total_time.start(); assert(rep.modes.size() >= 3); Ktensor ls_ktensor{}; Matrix ten_workspace{}; - cals::ops::LineSearchParams ls_params{}; + ls::LineSearchParams ls_params{}; if (rep.line_search) { if (rep.modes.size() == 3) @@ -109,50 +87,60 @@ namespace cals // Allocate and Initialize Gramians vector auto gramians = vector(rep.n_modes); - for (auto n = 0; n < rep.n_modes; n++) gramians[n] = Matrix(ktensor.get_rank(), ktensor.get_rank()); + for (auto n = 0; n < rep.n_modes; n++) gramians[n] = Matrix(ktensor.get_components(), ktensor.get_components()); for (auto n = 0; n < rep.n_modes; n++) cals::ops::update_gramian(ktensor.get_factor(n), gramians[n]); // Create empty matrix to store last G for error calculation - Matrix G_last(rep.modes[rep.n_modes - 1], ktensor.get_rank()); + Matrix G_last(rep.modes[rep.n_modes - 1], ktensor.get_components()); // Calculate and Allocate Workspace for intermediate KRP and Twostep vector workspace; - vector modes = rep.modes; // Create a copy to sort it + vector modes = rep.modes; // Create a copy to sort it std::sort(modes.begin(), modes.end(), std::greater<>()); - vector sizes(std::ceil(static_cast(modes.size() - 1) / 2.0), - 0); // sizes holds the nrows of each workspace matrix for KRP + vector sizes(std::ceil(static_cast(modes.size() - 1) / 2.0), + 0); // sizes holds the nrows of each workspace matrix for KRP sizes[0] = modes[0] * modes[1]; // Two largest modes (Can be used as a workspace for twostep) - for (auto i = 1lu; i < sizes.size(); i++) sizes[i] = modes[i + 1] * sizes[i - 1]; + for (auto i = 1lu; i < sizes.size(); i++) + sizes[i] = modes[i + 1] * sizes[i - 1]; workspace.resize(sizes.size()); - for (auto i = 0lu; i < sizes.size(); i++) workspace[i] = Matrix(sizes[i], ktensor.get_rank()); + for (auto i = 0lu; i < sizes.size(); i++) + workspace[i] = Matrix(sizes[i], ktensor.get_components()); // Create and initialize MTTKRP params - cals::ops::MttkrpParams mttkrp_params; + cals::mttkrp::MttkrpParams mttkrp_params; mttkrp_params.method = params.mttkrp_method; + mttkrp_params.lut = params.mttkrp_lut; mttkrp_params.cuda = params.cuda; mttkrp_params.krp_params.cuda = params.cuda; Timer lut_timer; lut_timer.start(); - mttkrp_params.lut = cals::utils::read_lookup_table(rep.modes, get_threads()); - - if (!mttkrp_params.lut.empty()) + if (mttkrp_params.lut.keys_v.empty()) { - vector mttkrp_lut_keys; - mttkrp_lut_keys.reserve(mttkrp_params.lut.size()); - for (auto &[key, val] : mttkrp_params.lut[0]) - mttkrp_lut_keys.emplace_back(key); - mttkrp_params.lut_keys = std::move(mttkrp_lut_keys); + if (!params.suppress_lut_warning) + std::cerr << "LUT empty" << endl; + mttkrp_params.lut = mttkrp::read_lookup_table(rep.modes, get_threads(), params.cuda, params.suppress_lut_warning); } + lut_timer.stop(); // std::cout << "Lookup table creation took " << lut_timer.get_time() << "seconds." << std::endl; +#if CUDA_ENABLED + auto stream = cuda::create_stream(); +#endif + if (params.cuda) { #if CUDA_ENABLED - X.allocate_cudata(X.get_n_elements()); - X.send_to_device(); + if (!params.omp_enabled) + { + if (X.get_cudata() == nullptr) + { + X.allocate_cudata(X.get_n_elements()); + X.send_to_device(); + } + } for (int i = 0; i < ktensor.get_n_modes(); i++) { @@ -175,7 +163,7 @@ namespace cals rep.iter++; rep.flops_per_iteration = 0; - TIME(rep.als_timer.timers[ALS_TIMERS::ITERATION].start();) + als_timers[ALS_TIMERS::ITERATION].start(); if (rep.line_search && !((rep.iter + 1) % rep.line_search_interval)) { @@ -188,35 +176,23 @@ namespace cals { auto &factor = ktensor.get_factor(n); - TIME(rep.mode_timers[n].timers[MODE_TIMERS::TOTAL_MTTKRP].start();) - TIME(cals::utils::set_mttkrp_timers(rep, mttkrp_params, n);) + mode_timers[MODE_TIMERS::MTTKRP].start(); - auto &G = ops::mttkrp(X, ktensor, workspace, n, mttkrp_params); + auto &G = mttkrp::mttkrp(X, ktensor, workspace, n, mttkrp_params); - TIME(rep.mode_timers[n].timers[MODE_TIMERS::TOTAL_MTTKRP].stop();) - TIME(rep.flops_per_iteration += mttkrp_params.flops;) + mode_timers[MODE_TIMERS::MTTKRP].stop(); auto &H = cals::ops::hadamard_but_one(gramians, n); - if (params.cuda) - { -#if CUDA_ENABLED - cudaDeviceSynchronize(); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } - // Save the last G matrix for error calculation if (n == rep.n_modes - 1) { - TIME(rep.als_timer.timers[ALS_TIMERS::G_COPY].start();) - G_last.read(G); - TIME(rep.als_timer.timers[ALS_TIMERS::G_COPY].stop();) + als_timers[ALS_TIMERS::G_COPY].start(); + G_last.copy(G); + als_timers[ALS_TIMERS::G_COPY].stop(); } - TIME(rep.mode_timers[n].timers[MODE_TIMERS::UPDATE].start();) + mode_timers[MODE_TIMERS::UPDATE].start(); if (params.update_method == cals::update::UPDATE_METHOD::UNCONSTRAINED) cals::update::update_factor_unconstrained(factor, H); @@ -228,7 +204,7 @@ namespace cals if (params.cuda) { #if CUDA_ENABLED - ktensor.get_factor(n).send_to_device_async(mttkrp_params.cuda_params.streams[0]); + ktensor.get_factor(n).send_to_device_async(stream); #else std::cerr << "Not compiled with CUDA support" << std::endl; exit(EXIT_FAILURE); @@ -240,48 +216,59 @@ namespace cals if (params.cuda) { #if CUDA_ENABLED - cudaDeviceSynchronize(); + cudaStreamSynchronize(stream); #else std::cerr << "Not compiled with CUDA support" << std::endl; exit(EXIT_FAILURE); #endif } - TIME(rep.mode_timers[n].timers[MODE_TIMERS::UPDATE].stop();) + mode_timers[MODE_TIMERS::UPDATE].stop(); + +#if WITH_TIME + rep.flops_per_iteration += mttkrp_params.flops; + for (auto i = 0; i < MODE_TIMERS::LENGTH; i++) + mode_times(n * MODE_TIMERS::LENGTH + i, rep.iter - 1) = mode_timers[i].get_time(); + for (auto i = 0; i < MTTKRP_TIMERS::LENGTH; i++) + mttkrp_times(n * MTTKRP_TIMERS::LENGTH + i, rep.iter - 1) = mttkrp_params.mttkrp_timers[i].get_time(); +#endif } // for mode // Compute the approximation error - TIME(rep.als_timer.timers[ALS_TIMERS::ERROR].start();) + als_timers[ALS_TIMERS::ERROR].start(); ktensor.set_iters(rep.iter); - const auto old_error = ktensor.get_approx_error(); - cals::ops::hadamard_all(gramians); - const auto error = cals::ops::compute_fast_error(rep.X_norm, ktensor.get_lambda(), ktensor.get_last_factor(), - G_last, gramians[0]); + const auto error = cals::error::compute_fast_error(rep.X_norm, ktensor.get_lambda(), ktensor.get_last_factor(), + G_last, gramians[0]); - ktensor.set_approx_error(error); - ktensor.set_approx_error_diff(std::fabs(old_error - error)); + const auto old_error = ktensor.get_approximation_error(); + // Ensure error always decreases (first iteration excluded due to undefined initial error) + const auto error_diff = old_error - error; + if (rep.iter != 1 && (error_diff < -1e-8)) + std::cerr << std::scientific + << "error incr Ktensor: " << ktensor.get_id() << " Iter: " << std::setw(3) << rep.iter + << " || Error: " << std::setw(5) << error + << " || Old Error: " << std::setw(5) << old_error + << " || Diff: " << std::setw(5) << error_diff << endl; - if (!((rep.iter == 1) || (old_error - error >= -1e-4))) - std::cerr << "error not decr Ktensor: " << ktensor.get_id() << " Error: " << ktensor.get_approx_error() - << " Old Error: " << old_error << endl; // Ensure error always gets smaller + ktensor.set_approximation_error(error); + ktensor.calculate_new_fit(rep.X_norm); - const auto old_fit = ktensor.get_fit(); - const auto fit = 1 - std::fabs(ktensor.get_approx_error()) / rep.X_norm; - - ktensor.set_fit(fit); - ktensor.set_fit_diff(std::fabs(fit - old_fit)); - - TIME(rep.als_timer.timers[ALS_TIMERS::ERROR].stop();) + als_timers[ALS_TIMERS::ERROR].stop(); if (rep.line_search && !(rep.iter % rep.line_search_interval)) - cals::ops::line_search(ktensor, ls_ktensor, workspace[0], ten_workspace, gramians, X, rep.X_norm, ls_params); + ls::line_search(ktensor, ls_ktensor, workspace[0], ten_workspace, gramians, X, rep.X_norm, ls_params); + + als_timers[ALS_TIMERS::ITERATION].stop(); - TIME(rep.als_timer.timers[ALS_TIMERS::ITERATION].stop();) +#if WITH_TIME + for (auto i = 0; i < ALS_TIMERS::LENGTH; i++) + als_times(i, rep.iter - 1) = als_timers[i].get_time(); +#endif - DEBUG(cout << "CONVERGENCE " << rep.iter << " " << ktensor.get_approx_error() << endl;) + DEBUG(cout << "CONVERGENCE " << rep.iter << " " << ktensor.get_approximation_error() << endl;) if (!params.force_max_iter) converged = (ktensor.get_fit_diff() < rep.tol) || (rep.iter >= rep.max_iter); @@ -291,9 +278,44 @@ namespace cals if (params.force_max_iter) converged = (rep.iter >= rep.max_iter); } while (!converged); - TIME(rep.als_timer.timers[AlsTimer::TOTAL].stop();) + total_time.stop(); + rep.total_time = total_time.get_time(); + +#if CUDA_ENABLED + cuda::destroy_stream(stream); +#endif + DEBUG(cout << "done." << endl;) return rep; } + + std::vector cp_omp_als(const Tensor &X, std::vector &ktensor_v, AlsParams ¶ms) + { + cals::Timer als_omp_time; + als_omp_time.start(); + + const auto n_ktensors = ktensor_v.size(); + +#if CUDA_ENABLED + X.allocate_cudata(X.get_n_elements()); + X.send_to_device(); + params.omp_enabled = true; +#endif + + vector reports(n_ktensors); +#pragma omp parallel for + for (auto i = 0lu; i < n_ktensors; i++) + reports[i] = cals::cp_als(X, ktensor_v[i], params); + als_omp_time.stop(); + +#if CUDA_ENABLED + params.omp_enabled = false; +#endif + + for (auto &rep : reports) + rep.total_time = als_omp_time.get_time(); + + return reports; + } } diff --git a/src/cals.cpp b/src/cals.cpp index 28cf7aa..6d03dc2 100644 --- a/src/cals.cpp +++ b/src/cals.cpp @@ -1,46 +1,12 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #include "cals.h" #include +#include -#include "utils.h" -#include +#include "multi_ktensor.h" +#include "utils/utils.h" +#include "utils/error.h" +#include "utils/line_search.h" #if CUDA_ENABLED @@ -50,8 +16,9 @@ using std::cout; using std::endl; -using ALS_TIMERS = cals::AlsTimer::ALS_TIMERS; -using MODE_TIMERS = cals::ModeTimer::MODE_TIMERS; +using ALS_TIMERS = cals::AlsTimers::TIMERS; +using MODE_TIMERS = cals::ModeTimers::TIMERS; +using MTTKRP_TIMERS = cals::MttkrpTimers::TIMERS; namespace cals { @@ -65,9 +32,9 @@ namespace cals cout << "]" << endl; ) - Timer total_time; - Timer init_time; CalsReport rep; + DEBUG(cout << "OMP_NUM_THREADS: " << omp_get_max_threads() << endl;) + DEBUG(cout << "Threads set: " << get_threads() << endl;) rep.tensor_rank = X.get_rank(); rep.n_modes = X.get_n_modes(); @@ -87,16 +54,33 @@ namespace cals rep.line_search_interval = params.line_search_interval; rep.line_search_step = params.line_search_step; - TIME(auto timer_size = std::ceil(20.0 * kt_queue.size() / rep.buffer_size) * (rep.max_iter + 1);); - TIME(rep.init_timers(rep.modes.size(), timer_size);) - TIME(for (auto &timer : rep.als_timer) timer.timers[cals::AlsTimer::TOTAL].start();) +#if WITH_TIME + auto timer_size = std::ceil(20.0 * kt_queue.size() / rep.buffer_size) * rep.max_iter; + rep.als_times = Matrix(ALS_TIMERS::LENGTH, timer_size); + rep.mode_times = Matrix(MODE_TIMERS::LENGTH * rep.n_modes, timer_size); + rep.mttkrp_times = Matrix(MTTKRP_TIMERS::LENGTH * rep.n_modes, timer_size); + auto &als_times = rep.als_times; + auto &mode_times = rep.mode_times; + auto &mttkrp_times = rep.mttkrp_times; + rep.flops_per_iteration.resize(timer_size, 0llu); + rep.cols.resize(timer_size, 0); +#endif + + Timer total_time; + Timer init_time; + AlsTimers als_timers; + ModeTimers mode_timers; + + using ALS_TIMERS = cals::AlsTimers::TIMERS; + + total_time.start(); assert(rep.modes.size() >= 3); total_time.start(); init_time.start(); Matrix ten_workspace{}; - cals::ops::LineSearchParams ls_params{}; + ls::LineSearchParams ls_params{}; if (rep.line_search) { if (rep.modes.size() == 3) @@ -128,9 +112,9 @@ namespace cals // Calculate and Allocate Workspace for intermediate KRP and Twostep vector workspace; - vector modes = rep.modes; // Create a copy to sort it + vector modes = rep.modes; // Create a copy to sort it std::sort(modes.begin(), modes.end(), std::greater<>()); - vector sizes(std::ceil(static_cast(modes.size() - 1) / 2.0), + vector sizes(std::ceil(static_cast(modes.size() - 1) / 2.0), 0); // sizes holds the nrows of each workspace matrix for KRP sizes[0] = modes[0] * modes[1]; // Two largest modes (Can be used as a workspace for twostep) for (auto i = 1lu; i < sizes.size(); i++) sizes[i] = modes[i + 1] * sizes[i - 1]; @@ -144,42 +128,53 @@ namespace cals remove_ids.reserve(100); // Create and initialize MTTKRP params - cals::ops::MttkrpParams mttkrp_params; + mttkrp::MttkrpParams mttkrp_params; mttkrp_params.method = params.mttkrp_method; mttkrp_params.cuda = params.cuda; mttkrp_params.krp_params.cuda = params.cuda; + mttkrp_params.lut = params.mttkrp_lut; init_time.stop(); - Timer lut_timer; - lut_timer.start(); - mttkrp_params.lut = cals::utils::read_lookup_table(rep.modes, get_threads()); - - if (!mttkrp_params.lut.empty()) - { - vector mttkrp_lut_keys; - mttkrp_lut_keys.reserve(mttkrp_params.lut.size()); - for (auto &[key, val] : mttkrp_params.lut[0]) - mttkrp_lut_keys.emplace_back(key); - mttkrp_params.lut_keys = std::move(mttkrp_lut_keys); - } - lut_timer.stop(); - std::cout << "Lookup table creation took " << lut_timer.get_time() << " seconds." << std::endl; - std::cout << "Initialization took " << init_time.get_time() << " seconds." << std::endl; +#if CUDA_ENABLED + auto stream = cuda::create_stream(); +#endif if (params.cuda) { #if CUDA_ENABLED - X.allocate_cudata(X.get_n_elements()); - X.send_to_device_async(mttkrp_params.cuda_params.streams[0]); + if (X.get_cudata() == nullptr) + { + X.allocate_cudata(X.get_n_elements()); + X.send_to_device_async(stream); + } for (auto &mf : mkt.get_factors()) mf.allocate_cudata(mf.get_max_n_elements()); for (auto &w : workspace) w.allocate_cudata(w.get_n_elements()); +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } - cuda::init_custream(); + Timer lut_timer; + lut_timer.start(); + if (mttkrp_params.lut.keys_v.empty()) + { + std::cerr << "LUT empty" << endl; + mttkrp_params.lut = mttkrp::read_lookup_table(rep.modes, get_threads(), params.cuda, false); + } + lut_timer.stop(); + //std::cout << "Lookup table creation took " << lut_timer.get_time() << " seconds." << std::endl; + //std::cout << "Initialization took " << init_time.get_time() << " seconds." << std::endl; + + if (params.cuda) + { +#if CUDA_ENABLED + cudaStreamSynchronize(stream); #else std::cerr << "Not compiled with CUDA support" << std::endl; exit(EXIT_FAILURE); @@ -192,9 +187,9 @@ namespace cals { mkt.set_iters(mkt.get_iters() + 1); rep.iter = mkt.get_iters(); - cout << "Iteration: " << rep.iter << endl; + // cout << "Iteration: " << rep.iter << endl; - TIME(rep.als_timer[rep.iter].timers[ALS_TIMERS::ITERATION].start();) + als_timers[ALS_TIMERS::ITERATION].start(); while (!kt_queue.empty()) { @@ -203,7 +198,7 @@ namespace cals Ktensor &ktensor = kt_queue.front(); mkt.add(ktensor); rep.n_ktensors++; - rep.ktensor_rank_sum += ktensor.get_rank(); + rep.ktensor_rank_sum += ktensor.get_components(); kt_queue.pop(); } catch (BufferFull &e) @@ -221,54 +216,35 @@ namespace cals auto &val = registry.at(keys_v[i]); if (rep.line_search && !((val.ktensor.get_iters() + 1) % rep.line_search_interval)) { + // TODO: create a Ktensor copy function to perform this copy with a single call. for (auto j = 0; j < rep.n_modes; j++) val.ls_ktensor.get_factor(j).copy(val.ktensor.get_factor(j)); } - } - if (params.cuda) - { -#if CUDA_ENABLED - cudaDeviceSynchronize(); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); +#if WITH_TIME + rep.cols[rep.iter - 1] = mkt.get_factor(0).get_cols(); + rep.flops_per_iteration[rep.iter - 1] = 0; #endif - } - - TIME(rep.cols[rep.iter] = mkt.get_factor(0).get_cols();) // Loop over the modes for (auto n = 0; n < rep.n_modes; n++) { - TIME(rep.mode_timers[rep.iter][n].timers[MODE_TIMERS::TOTAL_MTTKRP].start();) - TIME(cals::utils::set_mttkrp_timers(rep, mttkrp_params, n, rep.iter);) - - auto &G = ops::mttkrp(X, mkt, workspace, n, mttkrp_params); + mode_timers[MODE_TIMERS::MTTKRP].start(); - TIME(rep.mode_timers[rep.iter][n].timers[MODE_TIMERS::TOTAL_MTTKRP].stop();) - TIME(rep.flops_per_iteration[rep.iter] += mttkrp_params.flops;) + auto &G = mttkrp::mttkrp(X, mkt, workspace, n, mttkrp_params); - if (params.cuda) - { -#if CUDA_ENABLED - cudaDeviceSynchronize(); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } + mode_timers[MODE_TIMERS::MTTKRP].stop(); // Save the last G matrix for error calculation if (n == rep.n_modes - 1) { - TIME(rep.als_timer[rep.iter].timers[ALS_TIMERS::G_COPY].start();) + als_timers[ALS_TIMERS::G_COPY].start(); G_last.resize(G.get_rows(), G.get_cols()).copy(G); - TIME(rep.als_timer[rep.iter].timers[ALS_TIMERS::G_COPY].stop();) + als_timers[ALS_TIMERS::G_COPY].stop(); } - TIME(rep.mode_timers[rep.iter][n].timers[MODE_TIMERS::UPDATE].start();) + mode_timers[MODE_TIMERS::UPDATE].start(); #pragma omp parallel for // NOLINT(openmp-use-default-none) for (auto i = 0lu; i < keys_v.size(); i++) @@ -282,6 +258,7 @@ namespace cals update::update_factor_non_negative_constrained(val.ktensor.get_factor(n), val.gramians[n], val.ktensor.get_active_set(n)); + // Question: rep.iter or val.ktensor.get_iter? val.ktensor.normalize(n, rep.iter); ops::update_gramian(val.ktensor.get_factor(n), val.gramians[n]); @@ -291,85 +268,92 @@ namespace cals { #if CUDA_ENABLED mkt.get_factor(n).send_to_device(); - cudaDeviceSynchronize(); #else std::cerr << "Not compiled with CUDA support" << std::endl; exit(EXIT_FAILURE); #endif } - TIME(rep.mode_timers[rep.iter][n].timers[MODE_TIMERS::UPDATE].stop();) + mode_timers[MODE_TIMERS::UPDATE].stop(); + +#if WITH_TIME + rep.flops_per_iteration[rep.iter - 1] += mttkrp_params.flops; + for (auto i = 0; i < MODE_TIMERS::LENGTH; i++) + mode_times(n * MODE_TIMERS::LENGTH + i, rep.iter - 1) = mode_timers[i].get_time(); + for (auto i = 0; i < MTTKRP_TIMERS::LENGTH; i++) + mttkrp_times(n * MTTKRP_TIMERS::LENGTH + i, rep.iter - 1) = mttkrp_params.mttkrp_timers[i].get_time(); +#endif } // for mode // Compute the approximation error - TIME(rep.als_timer[rep.iter].timers[ALS_TIMERS::ERROR].start();) + als_timers[ALS_TIMERS::ERROR].start(); #pragma omp parallel for // NOLINT(openmp-use-default-none) for (auto i = 0lu; i < keys_v.size(); i++) { auto &val = registry.at(keys_v[i]); - const auto old_error = val.ktensor.get_approx_error(); - - const auto &ktensor_G_last = Matrix(val.ktensor.get_last_factor().get_rows(), val.ktensor.get_rank(), + const auto &ktensor_G_last = Matrix(val.ktensor.get_last_factor().get_rows(), val.ktensor.get_components(), G_last.get_data() + (val.col - mkt.get_start()) * G_last.get_col_stride()); cals::ops::hadamard_all(val.gramians); - const auto error = ops::compute_fast_error(rep.X_norm, val.ktensor.get_lambda(), val.ktensor.get_last_factor(), - ktensor_G_last, val.gramians[0]); - val.ktensor.set_approx_error(error); - val.ktensor.set_approx_error_diff(std::fabs(old_error - error)); - assert((val.ktensor.get_iters() == 1) || (old_error - error >= -1e-5)); // Make sure error decreases - - const auto old_fit = val.ktensor.get_fit(); - const auto fit = 1 - std::fabs(val.ktensor.get_approx_error()) / rep.X_norm; - val.ktensor.set_fit(fit); - val.ktensor.set_fit_diff(std::fabs(old_fit - fit)); + const auto error = error::compute_fast_error(rep.X_norm, val.ktensor.get_lambda(), + val.ktensor.get_last_factor(), + ktensor_G_last, val.gramians[0]); + assert((val.ktensor.get_iters() == 1) || + (val.ktensor.get_approximation_error() - error >= -1e-5)); // Make sure error decreases + val.ktensor.set_approximation_error(error); + val.ktensor.calculate_new_fit(rep.X_norm); } - // Don't parallelize until you figure out way to overcome ten_workspace limitation for (auto i = 0lu; i < keys_v.size(); i++) { auto &val = registry.at(keys_v[i]); if (rep.line_search && !(val.ktensor.get_iters() % (rep.line_search_interval))) - cals::ops::line_search(val.ktensor, val.ls_ktensor, workspace[0], ten_workspace, val.gramians, X, rep.X_norm, - ls_params); + ls::line_search(val.ktensor, val.ls_ktensor, workspace[0], ten_workspace, val.gramians, X, rep.X_norm, + ls_params); } - TIME(rep.als_timer[rep.iter].timers[ALS_TIMERS::ERROR].stop();) + + als_timers[ALS_TIMERS::ERROR].stop(); remove_ids.clear(); for (auto &[key, val] : registry) - if (!params.force_max_iter) + if (!params.always_evict_first) { - if (val.ktensor.get_fit_diff() < rep.tol || val.ktensor.get_iters() >= rep.max_iter) + if (!params.force_max_iter) + { + if (val.ktensor.get_fit_diff() < rep.tol || val.ktensor.get_iters() >= rep.max_iter) + remove_ids.push_back(key); + else + val.ktensor.set_iters(val.ktensor.get_iters() + 1); + } else if (val.ktensor.get_iters() >= rep.max_iter) remove_ids.push_back(key); else val.ktensor.set_iters(val.ktensor.get_iters() + 1); + } else // Evict leftmost model for experiments + { + int id = mkt.get_leftmost_id(); + if (id != -1) + remove_ids.push_back(id); + break; } - else if (val.ktensor.get_iters() >= rep.max_iter) - remove_ids.push_back(key); - else - val.ktensor.set_iters(val.ktensor.get_iters() + 1); for (auto &key : remove_ids) mkt.remove(key); - if (params.cuda) - { -#if CUDA_ENABLED - cudaDeviceSynchronize(); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } // Compression + als_timers.timers[ALS_TIMERS::DEFRAGMENTATION].start(); mkt.compress(); + als_timers.timers[ALS_TIMERS::DEFRAGMENTATION].stop(); - TIME(rep.als_timer[rep.iter].timers[ALS_TIMERS::ITERATION].stop();) + als_timers[ALS_TIMERS::ITERATION].stop(); +#if WITH_TIME + for (auto i = 0; i < ALS_TIMERS::LENGTH; i++) + als_times(i, rep.iter - 1) = als_timers[i].get_time(); +#endif DEBUG( cout << "CONVERGENCE " << rep.iter << endl; - for (auto &[key, val]: registry) cout << val.ktensor.get_approx_error() << " "; + for (auto &[key, val]: registry) cout << val.ktensor.get_approximation_error() << " "; cout << endl; ) @@ -387,31 +371,13 @@ namespace cals mkt.remove(key); } - if (params.cuda) - { #if CUDA_ENABLED - cudaDeviceSynchronize(); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); + cuda::destroy_stream(stream); #endif - } - - TIME(for (auto &timer : rep.als_timer) timer.timers[cals::AlsTimer::TOTAL].stop();) total_time.stop(); - - cout << "Computation time: " << total_time.get_time() << endl; - - if (params.cuda) - { -#if CUDA_ENABLED - cuda::destroy_custream(); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } + rep.total_time = total_time.get_time(); + // cout << "Computation time: " << total_time.get_time() << endl; DEBUG(cout << "done." << endl;) diff --git a/src/cals_blas.cpp b/src/cals_blas.cpp index 6992a95..e098eb7 100644 --- a/src/cals_blas.cpp +++ b/src/cals_blas.cpp @@ -1,46 +1,6 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #include "cals_blas.h" -#include "omp.h" - -void set_threads(int threads) -{ +void set_threads(int threads) { omp_set_num_threads(threads); #if CALS_MKL mkl_set_num_threads(threads); @@ -48,21 +8,19 @@ void set_threads(int threads) bli_thread_set_num_threads(threads); #elif CALS_OPENBLAS openblas_set_num_threads(threads); -#elif CALS_Matlab +#elif CALS_MATLAB omp_set_num_threads(threads); #endif - } -int get_threads() -{ +int get_threads() { #if CALS_MKL return mkl_get_max_threads(); #elif CALS_BLIS return bli_thread_get_num_threads(); #elif CALS_OPENBLAS return openblas_get_num_threads(); -#elif CALS_Matlab +#elif CALS_MATLAB return omp_get_max_threads(); #endif } diff --git a/src/cuda_utils.cpp b/src/cuda_utils.cpp index c5f4a95..973f906 100644 --- a/src/cuda_utils.cpp +++ b/src/cuda_utils.cpp @@ -1,40 +1,3 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #if CUDA_ENABLED #include "cuda_utils.h" @@ -45,161 +8,134 @@ #include "tensor.h" -namespace cuda -{ - void identify_devices() - { - int n_devices = 0; - cudaGetDeviceCount(&n_devices); - std::cout << "Number of devices: " << n_devices << std::endl; - - for (auto id = 0; id < n_devices; id++) - { - cudaDeviceProp prop{}; - cudaGetDeviceProperties(&prop, id); - printf("Device Number: %d\n", id); - printf(" Device name: %s\n", prop.name); - printf(" Memory Clock Rate (KHz): %d\n", prop.memoryClockRate); - printf(" Memory Bus Width (bits): %d\n", prop.memoryBusWidth); - printf(" Peak Memory Bandwidth (GB/s): %f\n\n", 2.0*prop.memoryClockRate*(prop.memoryBusWidth/8)/1.0e6); - } +namespace cuda { +void identify_devices() { + int n_devices = 0; + cudaGetDeviceCount(&n_devices); + std::cout << "Number of devices: " << n_devices << std::endl; + + for (auto id = 0; id < n_devices; id++) { + cudaDeviceProp prop{}; + cudaGetDeviceProperties(&prop, id); + printf("Device Number: %d\n", id); + printf(" Device name: %s\n", prop.name); + printf(" Memory Clock Rate (KHz): %d\n", prop.memoryClockRate); + printf(" Memory Bus Width (bits): %d\n", prop.memoryBusWidth); + printf(" Peak Memory Bandwidth (GB/s): %f\n\n", 2.0 * prop.memoryClockRate * (prop.memoryBusWidth / 8) / 1.0e6); } +} - void allocate(double* &d_A, long int size) - { - if (cudaMalloc(reinterpret_cast(&d_A), size * sizeof(double)) != cudaSuccess) - std::cerr << "Device memory allocation error" << std::endl; - } +void allocate(double *&d_A, long int size) { + if (cudaMalloc(reinterpret_cast(&d_A), size * sizeof(double)) != cudaSuccess) + std::cerr << "Device memory allocation error" << std::endl; +} - void deallocate(double* &d_A) - { - auto error = cudaFree(d_A); - if (error != cudaSuccess) - std::cerr << "Memory free error: " << error << std::endl; - } +void deallocate(double *&d_A) { + auto error = cudaFree(d_A); + if (error != cudaSuccess) + std::cerr << "Memory free error: " << error << std::endl; +} - void allocate_async(double* &host_ptr, long int size) - { - auto error = cudaHostAlloc((void **) &host_ptr, size * sizeof(double), cudaHostAllocDefault); +void allocate_async(double *&host_ptr, long int size) { + auto error = cudaHostAlloc((void **)&host_ptr, size * sizeof(double), cudaHostAllocDefault); - if (error != cudaSuccess) - { - std::cerr << "Cannot allocate memory on host: " << error << std::endl; - exit(EXIT_FAILURE); - } + if (error != cudaSuccess) { + std::cerr << "Cannot allocate memory on host: " << error << std::endl; + exit(EXIT_FAILURE); } +} - void deallocate_async(double* &host_ptr) - { - auto error = cudaFreeHost(host_ptr); +void deallocate_async(double *&host_ptr) { + auto error = cudaFreeHost(host_ptr); - if (error != cudaSuccess) - { - std::cerr << "Cannot allocate memory on host: " << error << std::endl; - exit(EXIT_FAILURE); - } + if (error != cudaSuccess) { + std::cerr << "Cannot allocate memory on host: " << error << std::endl; + exit(EXIT_FAILURE); } +} - void send_to_device(double* h_A, double* d_A, int size) - { - auto status = cublasSetVector(size, sizeof(double), h_A, 1, d_A, 1); +void send_to_device(double *h_A, double *d_A, int size) { + auto status = cublasSetVector(size, sizeof(double), h_A, 1, d_A, 1); - if (status != CUBLAS_STATUS_SUCCESS) - { - std::cerr << "Data transfer error to Device. Return value: " << status << std::endl; - exit(EXIT_FAILURE); - } + if (status != CUBLAS_STATUS_SUCCESS) { + std::cerr << "Data transfer error to Device. Return value: " << status << std::endl; + exit(EXIT_FAILURE); } +} - void send_to_device_async(double* h_A, double* d_A, int size, cudaStream_t &stream) - { - auto error = cudaMemcpyAsync(d_A, h_A, size * sizeof(double), cudaMemcpyHostToDevice, stream); - if (error != cudaSuccess) - { - std::cerr << "Data transfer error to Device: " << error << std::endl; - exit(EXIT_FAILURE); - } - } - - void receive_from_device(double* d_A, double* h_A, int size) - { - auto status = cublasGetVector(size, sizeof(double), d_A, 1, h_A, 1); - - if (status != CUBLAS_STATUS_SUCCESS) - { - std::cerr << "Data transfer error from Device: " << status << std::endl; - exit(EXIT_FAILURE); - } +void send_to_device_async(double *h_A, double *d_A, int size, cudaStream_t &stream) { + auto error = cudaMemcpyAsync(d_A, h_A, size * sizeof(double), cudaMemcpyHostToDevice, stream); + if (error != cudaSuccess) { + std::cerr << "Data transfer error to Device: " << error << std::endl; + exit(EXIT_FAILURE); } +} - void receive_from_device_async(double* d_A, double* h_A, int size, cudaStream_t &stream) - { - auto error = cudaMemcpyAsync(h_A, d_A, size * sizeof(double), cudaMemcpyDeviceToHost, stream); +void receive_from_device(double *d_A, double *h_A, int size) { + auto status = cublasGetVector(size, sizeof(double), d_A, 1, h_A, 1); - if (error != cudaSuccess) - { - std::cerr << "Data transfer error from Device: " << error << std::endl; - exit(EXIT_FAILURE); - } + if (status != CUBLAS_STATUS_SUCCESS) { + std::cerr << "Data transfer error from Device: " << status << std::endl; + exit(EXIT_FAILURE); } +} - void initialize_stream(cudaStream_t &stream) - { - auto status = cudaStreamCreate(&stream); +void receive_from_device_async(double *d_A, double *h_A, int size, cudaStream_t &stream) { + auto error = cudaMemcpyAsync(h_A, d_A, size * sizeof(double), cudaMemcpyDeviceToHost, stream); - if (status != cudaSuccess) - std::cerr << "Stream creation failed." << std::endl; + if (error != cudaSuccess) { + std::cerr << "Data transfer error from Device: " << error << std::endl; + exit(EXIT_FAILURE); } +} - cudaStream_t create_stream() - { - cudaStream_t stream; - auto status = cudaStreamCreate(&stream); +void initialize_stream(cudaStream_t &stream) { + auto status = cudaStreamCreate(&stream); - if (status != cudaSuccess) - std::cerr << "Stream creation failed." << std::endl; + if (status != cudaSuccess) + std::cerr << "Stream creation failed." << std::endl; +} - return stream; - } +cudaStream_t create_stream() { + cudaStream_t stream; + auto status = cudaStreamCreate(&stream); - void destroy_stream(cudaStream_t &stream) - { - auto status = cudaStreamDestroy(stream); + if (status != cudaSuccess) + std::cerr << "Stream creation failed." << std::endl; - if (status != cudaSuccess) - std::cerr << "Stream creation failed." << std::endl; - } + return stream; } -namespace cuda::cublas -{ - cublasHandle_t create_handle() - { - cublasHandle_t handle; - auto status = cublasCreate(&handle); +void destroy_stream(cudaStream_t &stream) { + auto status = cudaStreamDestroy(stream); - if (status != CUBLAS_STATUS_SUCCESS) - std::cerr << "CUBLAS initialization error" << std::endl; + if (status != cudaSuccess) + std::cerr << "Stream creation failed." << std::endl; +} +} // namespace cuda - return handle; - } +namespace cuda::cublas { +cublasHandle_t create_handle() { + cublasHandle_t handle; + auto status = cublasCreate_v2(&handle); - void destroy_handle(cublasHandle_t &handle) - { - auto status = cublasDestroy(handle); + if (status != CUBLAS_STATUS_SUCCESS) + std::cerr << "CUBLAS initialization error" << std::endl; - if (status != CUBLAS_STATUS_SUCCESS) - std::cerr << "CUBLAS shutdown error"; - } + return handle; } -namespace cuda -{ +void destroy_handle(cublasHandle_t &handle) { + auto status = cublasDestroy(handle); + if (status != CUBLAS_STATUS_SUCCESS) + std::cerr << "CUBLAS shutdown error"; } +} // namespace cuda::cublas + +namespace cuda {} -int test_func() -{ +int test_func() { double *a = nullptr; auto handle = cuda::cublas::create_handle(); diff --git a/src/examples/CMakeLists.txt b/src/examples/CMakeLists.txt new file mode 100644 index 0000000..4d5a8b7 --- /dev/null +++ b/src/examples/CMakeLists.txt @@ -0,0 +1,3 @@ +add_executable(driver driver.cpp) +add_compilation_flags(driver "${opt_flags}") +target_link_libraries(driver PUBLIC ${CP_CALS}) diff --git a/src/examples/cuda_driver.cpp b/src/examples/cuda_driver.cpp deleted file mode 100644 index a22a93b..0000000 --- a/src/examples/cuda_driver.cpp +++ /dev/null @@ -1,139 +0,0 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - -#include -#include - -#include "cals.h" -#include "als.h" -#include "cals_blas.h" - -using std::cout; -using std::endl; - -int main(int argc, char *argv[]) -{ - ///////////////////////////////////////////////////////////////////////////////// - // Set the number of threads. - ///////////////////////////////////////////////////////////////////////////////// - int num_threads = 4; - set_threads(num_threads); - - ///////////////////////////////////////////////////////////////////////////////// - // Create target Tensor. - ///////////////////////////////////////////////////////////////////////////////// - vector modes {210, 210, 210}; - cals::Tensor X(modes); - X.randomize(); - - ///////////////////////////////////////////////////////////////////////////////// - // Create input Ktensors. - ///////////////////////////////////////////////////////////////////////////////// - - // Create a vector with all the ranks we want to run CP-ALS for. - vector ranks; - for (auto rank = 1; rank <= 5; rank++) // Ranks from 1 up to (and including) 5 - for (auto cp = 0; cp < 10; cp++) // 10 copies of each rank - ranks.push_back(rank); - - // Create a vector containing the Ktensors we want to fit to the target Tensor. - vector cals_input(ranks.size()); - auto i = 0; - for (auto &ktensor : cals_input) - { - ktensor = cals::Ktensor(ranks[i++], modes); - ktensor.randomize(); - } - cout << "Created " << ranks.size() << " Ktensors." << endl; - cout << "Ktensor ranks: "; - for (auto &r : ranks) cout << r << " "; - cout << endl; - - // Copy the same Ktensors to be used as input to regular ALS. - auto als_input(cals_input); - - ///////////////////////////////////////////////////////////////////////////////// - // Run CALS. - ///////////////////////////////////////////////////////////////////////////////// - cout << "Running CALS..." << endl; - - // Create the input to CALS, as a Queue of references to the Ktensors created. - cals::KtensorQueue cals_queue; - for (auto &p : cals_input) - cals_queue.emplace(p); - - // Set the CALS parameters (if necessary). - cals::CalsParams cals_params; - cals_params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - cals_params.force_max_iter = true; - cals_params.buffer_size = std::accumulate(ranks.cbegin(), ranks.cend(), 0); - cals_params.max_iterations = 50; - cals_params.tol = 1e-4; - cals_params.cuda = true; - - cals::Timer cals_timer; - cals_timer.start(); - auto cals_report = cp_cals(X, cals_queue, cals_params); - cals_timer.stop(); - - ///////////////////////////////////////////////////////////////////////////////// - // Run a for-loop of regular ALS for comparison. - ///////////////////////////////////////////////////////////////////////////////// - cout << "Running ALS..." << endl; - - // Set the ALS parameters (if necessary). - cals::AlsParams als_params; - als_params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - als_params.force_max_iter = true; - als_params.max_iterations = 50; - als_params.tol = 1e-4; - - cals::Timer als_timer; - als_timer.start(); - for (auto &kt : als_input) - auto als_report = cp_als(X, kt, als_params); - als_timer.stop(); - - ///////////////////////////////////////////////////////////////////////////////// - // Print timings. - ///////////////////////////////////////////////////////////////////////////////// - - cout << "======================================================================" << endl; - cout << "ALS time: " << als_timer.get_time() << endl; - cout << "CALS time: " << cals_timer.get_time() << endl; - cout << "Speedup: " << als_timer.get_time() / cals_timer.get_time() << endl; -} diff --git a/src/examples/driver.cpp b/src/examples/driver.cpp index d81992a..e143bc2 100644 --- a/src/examples/driver.cpp +++ b/src/examples/driver.cpp @@ -1,62 +1,135 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #include #include +#include -#include "cals.h" #include "als.h" -#include "cals_blas.h" +#include "cals.h" +using std::cerr; using std::cout; using std::endl; +using std::string; +using std::vector; + +void show_usage(char *text) { + cout << "USAGE: " << text << "[Options] " << endl; + cout << endl; + cout << "-n --nthreads THREADS" << endl; + cout << " Set the number of threads (defaults to OMP_NUM_THREADS, if set, or else to 10)" << endl; + cout << "-c --components MIN:MAX:COPIES" << endl; + cerr << " Set the MIN and MAX number of components per model, as well as the randomly initialized COPIES of " + "each model. (defaults to 1:10:10)" + << endl; + cout << "-t --tensor DIM0-DIM1-DIM2" << endl; + cerr << " Set the DIMensions of the target tensor." << endl; + cout << endl; +} + +void string_split(const std::string &str, vector &vect, char delimiter = ' ') { + std::stringstream ss(str); + std::string token; + while (std::getline(ss, token, delimiter)) + vect.push_back(std::strtoul(token.c_str(), nullptr, 10)); +} + +int main(int argc, char *argv[]) { + cout << "GENERAL INFO" << endl; + cout << "=======================================================================================" << endl; + cout << "The `driver` executable demonstrates how CALS runs without specifying extra arguments. " << endl; + cout << "The associated source file is located in `src/examples/driver.cpp`." << endl; + cout << "=======================================================================================" << endl; + cout << endl; + + ///////////////////////////////////////////////////////////////////////////////// + // Parse Arguments. + ///////////////////////////////////////////////////////////////////////////////// + cout << "Parsing Arguments..." << endl; + vector modes{210, 210, 210}; + int min_components{1}; + int max_components{10}; + int mul_components{10}; + unsigned int num_threads{10}; + bool f_threads = false; + + for (int i = 1; i < argc; ++i) { + std::string arg = argv[i]; + if ((arg == "-h") || (arg == "--help")) { + show_usage(argv[0]); + return 0; + } else if ((arg == "-n") || (arg == "--nthreads")) { + if (i + 1 < argc) { + num_threads = static_cast(std::strtol(argv[++i], nullptr, 10)); + cout << "Threads specified: " << num_threads << endl; + f_threads = true; + } else { + cerr << "--nthreads option requires one argument." << endl; + return 1; + } + } else if ((arg == "-c") || (arg == "--components")) { + if (i + 1 < argc) { + vector split_components; + string_split(string(argv[++i]), split_components, ':'); + if (split_components.size() != 3) { + cerr << "--components option requires one argument of the form MIN:MAX:COPIES." << endl; + return 1; + } else { + min_components = static_cast(split_components[0]); + max_components = static_cast(split_components[1]); + mul_components = static_cast(split_components[2]); + } + } else { + cerr << "--components option requires one argument of the form MIN:MAX:COPIES." << endl; + return 1; + } + } else if ((arg == "-t") || (arg == "--tensor")) { + if (i + 1 < argc) { + vector split_tensor; + string_split(string(argv[++i]), split_tensor, '-'); + + if (split_tensor.size() < 3) { + cerr << "--tensor option requires one argument of the form DIM0-DIM1-DIM2." << endl; + return 1; + } else { + modes[0] = split_tensor[0]; + modes[1] = split_tensor[1]; + modes[2] = split_tensor[2]; + cout << "Tensor dimensions specified: "; + for (auto &s : split_tensor) + cout << s << " "; + cout << endl; + } + } else { + cerr << "--tensor option requires one argument of the form DIM0-DIM1-DIM2." << endl; + return 1; + } + } else { + cerr << "Unrecognized argument " << arg << endl; + show_usage(argv[0]); + return 1; + } + } + cout << "Done Parsing Arguments..." << endl; + cout << endl; -int main(int argc, char *argv[]) -{ ///////////////////////////////////////////////////////////////////////////////// // Set the number of threads. ///////////////////////////////////////////////////////////////////////////////// - int num_threads = 4; - set_threads(num_threads); + if (!f_threads) { + const char *omp_num_threads = std::getenv("OMP_NUM_THREADS"); + if (omp_num_threads != nullptr) { + num_threads = static_cast(std::strtol(omp_num_threads, nullptr, 10)); + cout << "OMP_NUM_THREADS: " << num_threads << endl; + } else { + num_threads = 10; + cout << "OMP_NUM_THREADS not set. Using default " << num_threads << " threads." << endl; + } + } + + set_threads(static_cast(num_threads)); ///////////////////////////////////////////////////////////////////////////////// // Create target Tensor. ///////////////////////////////////////////////////////////////////////////////// - vector modes {210, 210, 210}; cals::Tensor X(modes); X.randomize(); @@ -64,32 +137,32 @@ int main(int argc, char *argv[]) // Create input Ktensors. ///////////////////////////////////////////////////////////////////////////////// - // Create a vector with all the ranks we want to run CP-ALS for. - vector ranks; - for (auto rank = 1; rank <= 5; rank++) // Ranks from 1 up to (and including) 5 - for (auto cp = 0; cp < 10; cp++) // 10 copies of each rank - ranks.push_back(rank); + // Create a vector with all the numbers of components we want to run CP-ALS for. + vector components; + // Components from min_components up to (and including) max_components + for (auto comp = min_components; comp <= max_components; comp++) + for (auto cp = 0; cp < mul_components; cp++) // mul_components copies of each number of components + components.push_back(comp); - // Create a vector containing the Ktensors we want to fit to the target Tensor. - vector cals_input(ranks.size()); + // Create a vector containing the Ktensors (models) we want to fit to the target Tensor. + vector cals_input(components.size()); auto i = 0; - for (auto &ktensor : cals_input) - { - ktensor = cals::Ktensor(ranks[i++], modes); + for (auto &ktensor : cals_input) { + ktensor = cals::Ktensor(components[i++], modes); ktensor.randomize(); } - cout << "Created " << ranks.size() << " Ktensors." << endl; - cout << "Ktensor ranks: "; - for (auto &r : ranks) cout << r << " "; + cout << "Created " << components.size() << " Ktensors." << endl; + cout << "Ktensor components: "; + for (auto &r : components) + cout << r << " "; cout << endl; - // Copy the same Ktensors to be used as input to regular ALS. + // Copy the same Ktensors to be used as input to regular ALS to a new vector. auto als_input(cals_input); ///////////////////////////////////////////////////////////////////////////////// // Run CALS. ///////////////////////////////////////////////////////////////////////////////// - cout << "Running CALS..." << endl; // Create the input to CALS, as a Queue of references to the Ktensors created. cals::KtensorQueue cals_queue; @@ -98,34 +171,47 @@ int main(int argc, char *argv[]) // Set the CALS parameters (if necessary). cals::CalsParams cals_params; - cals_params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - cals_params.buffer_size = std::accumulate(ranks.cbegin(), ranks.cend(), 0); - cals_params.force_max_iter = true; - cals_params.max_iterations = 50; - cals_params.tol = 1e-4; + cals_params.mttkrp_method = cals::mttkrp::MTTKRP_METHOD::AUTO; + cals_params.update_method = cals::update::UPDATE_METHOD::UNCONSTRAINED; + // cals_params.force_max_iter = true; + cals_params.max_iterations = 1000; + cals_params.tol = 1e-5; + + cals_params.buffer_size = std::accumulate(components.cbegin(), components.cend(), static_cast(0)); + cals_params.print(); + cout << "Running CALS..." << endl; cals::Timer cals_timer; cals_timer.start(); auto cals_report = cp_cals(X, cals_queue, cals_params); cals_timer.stop(); + cout << "Finished CALS." << endl; + cout << endl; ///////////////////////////////////////////////////////////////////////////////// // Run a for-loop of regular ALS for comparison. ///////////////////////////////////////////////////////////////////////////////// - cout << "Running ALS..." << endl; // Set the ALS parameters (if necessary). cals::AlsParams als_params; - als_params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - als_params.force_max_iter = true; - als_params.max_iterations = 50; - als_params.tol = 1e-4; + als_params.mttkrp_method = cals::mttkrp::MTTKRP_METHOD::AUTO; + als_params.update_method = cals::update::UPDATE_METHOD::UNCONSTRAINED; + // als_params.force_max_iter = true; + als_params.max_iterations = 1000; + als_params.tol = 1e-5; + // Suppress the warning that no Lookup table was found, to not clutter the standard output. + als_params.suppress_lut_warning = true; + als_params.print(); + + cout << "Running ALS..." << endl; cals::Timer als_timer; als_timer.start(); for (auto &kt : als_input) auto als_report = cp_als(X, kt, als_params); als_timer.stop(); + cout << "Finished ALS." << endl; + cout << endl; ///////////////////////////////////////////////////////////////////////////////// // Print timings. diff --git a/src/experiments/CMakeLists.txt b/src/experiments/CMakeLists.txt new file mode 100644 index 0000000..0b15584 --- /dev/null +++ b/src/experiments/CMakeLists.txt @@ -0,0 +1,51 @@ +if (WITH_DIAGNOSTICS GREATER_EQUAL 1) + set(CMAKE_VERBOSE_MAKEFILE ON) +endif () + +add_executable(experiments experiments.cpp experiments_utils.cpp) +add_compilation_flags(experiments "${comp_flags}") +target_link_libraries(experiments PUBLIC ${CP_CALS}) + +add_executable(benchmark_cals_mttkrp benchmark_cals_mttkrp.cpp) +add_compilation_flags(benchmark_cals_mttkrp "${comp_flags}") +target_link_libraries(benchmark_cals_mttkrp PUBLIC ${CP_CALS}) + +add_executable(evaluate peak_evaluator.cpp) +add_compilation_flags(evaluate "${comp_flags}") +target_link_libraries(evaluate PUBLIC ${CP_CALS}) + +add_executable(benchmark_other_mttkrp benchmark_other_mttkrp.cpp) +add_compilation_flags(benchmark_other_mttkrp "${comp_flags}") +target_link_libraries(benchmark_other_mttkrp PUBLIC ${CP_CALS}) + +############################################################################################# +# Letter related experiments +add_executable(experiments_letter experiments_letter.cpp experiments_utils.cpp) +target_link_libraries(experiments_letter PUBLIC ${CP_CALS}) + +set(WITH_CTF "OFF") +set(WITH_PLANC "OFF") +if (WITH_EXPERIMENTS) + set(WITH_CTF "ON") + set(WITH_PLANC "ON") +endif() + +if (WITH_CTF) + set(CTF_PATH "${CMAKE_SOURCE_DIR}/extern/ctf") + find_package(MPI REQUIRED) + + target_compile_definitions(benchmark_other_mttkrp PUBLIC WITH_CTF=1) + target_include_directories(benchmark_other_mttkrp PUBLIC "${CTF_PATH}/include") + target_link_directories(benchmark_other_mttkrp PUBLIC "${CTF_PATH}/lib_shared") + target_link_libraries(benchmark_other_mttkrp PUBLIC ctf MPI::MPI_CXX) +endif () + +if (WITH_PLANC) + set(PLANC_PATH "${CMAKE_SOURCE_DIR}/extern/planc") + set(ARMA_PATH "${CMAKE_SOURCE_DIR}/extern/armadillo") + + include_directories(${PLANC_PATH}) + target_compile_definitions(benchmark_other_mttkrp PUBLIC WITH_PLANC=1) + target_include_directories(benchmark_other_mttkrp PUBLIC "${PLANC_PATH}/dimtree") + target_include_directories(benchmark_other_mttkrp PUBLIC "${ARMA_PATH}/include") +endif () \ No newline at end of file diff --git a/src/experiments/benchmark_cals_mttkrp.cpp b/src/experiments/benchmark_cals_mttkrp.cpp new file mode 100644 index 0000000..6e4698d --- /dev/null +++ b/src/experiments/benchmark_cals_mttkrp.cpp @@ -0,0 +1,58 @@ +#include +#include + +#include "experiments/bench_mttkrp.h" + +using cals::mttkrp::MTTKRP_METHOD; +using cals::mttkrp::MttkrpParams; +using std::cerr; +using std::cout; +using std::endl; + +int main(int argc, char *argv[]) { + cout << "=============================================================================" << endl; + cout << "This executable performs experiments regarding MTTKRP for the paper." << endl; + cout << "It benchmarks the different implementations of MTTKRP supported by CALS." << endl; + cout << "The corresponding source file is located in `src/experiments/benchmark_cals_mttkrp.cpp`" << endl; + cout << "The output is writen in CSV files in the `data` folder of the project." << endl; + cout << "This executable accepts only one (mandatory) argument, the number of threads." << endl; + cout << "=============================================================================" << endl; + cout << endl; + + if (argc != 2) { + cerr << "Not enough arguments. Give number of threads." << endl; + cout << "USAGE: " << argv[0] << " " << endl; + abort(); + } else { + std::string arg = argv[1]; + if ((arg == "-h") || (arg == "--help")) { + cout << "USAGE: " << argv[0] << " " << endl; + return 0; + } + } + +#if CUDA_ENABLED + bool cuda_f = true; +#else + bool cuda_f = false; +#endif + + int num_threads = std::atoi(argv[1]); // NOLINT(cert-err34-c) + + vector methods_v; + methods_v.push_back(MTTKRP_METHOD::MTTKRP); + methods_v.push_back(MTTKRP_METHOD::TWOSTEP0); + methods_v.push_back(MTTKRP_METHOD::TWOSTEP1); + + vector> modes_v; + modes_v.push_back({100, 100, 100}); + modes_v.push_back({200, 200, 200}); + modes_v.push_back({300, 300, 300}); + // modes_v.push_back({299, 301, 41}); + // modes_v.push_back({405, 136, 19}); + // modes_v.push_back({255, 281, 25}); + + for (auto method : methods_v) + for (auto &modes : modes_v) + cals::bench::benchmark_mttkrp(modes, num_threads, method, cuda_f); +} diff --git a/src/experiments/benchmark_other_mttkrp.cpp b/src/experiments/benchmark_other_mttkrp.cpp new file mode 100644 index 0000000..da87277 --- /dev/null +++ b/src/experiments/benchmark_other_mttkrp.cpp @@ -0,0 +1,62 @@ +#include +#include + +#include "experiments/bench_mttkrp.h" + +#if WITH_CTF + +#include "ctf.hpp" + +#endif + +using cals::bench::EXTERNAL_MTTKRP_METHODS; +using cals::mttkrp::MTTKRP_METHOD; +using std::cerr; +using std::cout; +using std::endl; + +int main(int argc, char *argv[]) { + cout << "=============================================================================" << endl; + cout << "This executable performs experiments for the letter." << endl; + cout << "It compares the implementation of MTTKRP in CALS with other implementations." << endl; + cout << "The corresponding source file is located in `src/experiments/benchmark_other_mttkrp.cpp`" << endl; + cout << "The output is writen in CSV files in the `data` folder of the project." << endl; + cout << "This executable accepts only one (mandatory) argument, the number of threads." << endl; + cout << "=============================================================================" << endl; + cout << endl; + + if (argc != 2) { + cerr << "Not enough arguments. Give number of threads." << endl; + cout << "USAGE: " << argv[0] << " " << endl; + abort(); + } else { + std::string arg = argv[1]; + if ((arg == "-h") || (arg == "--help")) { + cout << "USAGE: " << argv[0] << " " << endl; + return 0; + } + } + +#if WITH_CTF + int mpi_rank, np; + MPI_Init(&argc, &argv); + MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank); + MPI_Comm_size(MPI_COMM_WORLD, &np); +#endif + + int num_threads = std::atoi(argv[1]); // NOLINT(cert-err34-c) + + vector methods_v; + methods_v.push_back(MTTKRP_METHOD::AUTO); + methods_v.push_back(EXTERNAL_MTTKRP_METHODS::CTF); + methods_v.push_back(EXTERNAL_MTTKRP_METHODS::PLANC); + + vector> modes_v; + modes_v.push_back({100, 100, 100}); + modes_v.push_back({200, 200, 200}); + modes_v.push_back({300, 300, 300}); + + for (auto method : methods_v) + for (auto &modes : modes_v) + cals::bench::benchmark_mttkrp(modes, num_threads, method); +} diff --git a/src/experiments/cpu_experiments.cpp b/src/experiments/cpu_experiments.cpp deleted file mode 100644 index c8fa47c..0000000 --- a/src/experiments/cpu_experiments.cpp +++ /dev/null @@ -1,183 +0,0 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - -#include -#include -#include - -#include "experiments/experiments_utils.h" -#include "experiments/mttkrp_benchmarker.h" - -void generate_ranks(vector &ranks, int min, int max, int copies); - -int main(int argc, char *argv[]) -{ - if (argc != 2) - { - std::cerr << "Not enough arguments. Give number of threads."; - abort(); - } - - int num_threads = std::atoi(argv[1]); // NOLINT(cert-err34-c) - - // Mock run to warmup everything. - if (true) - { - vector ranks; - generate_ranks(ranks, 1, 20, 20); - std::sort(ranks.begin(), ranks.end()); - - cals::CalsParams params; - params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - params.max_iterations = 50; // TTB default - params.buffer_size = 4200; - params.tol = 1e-4; // TTB default - params.cuda = false; - - std::string file_name = "../data/fluorescence_cancer_UD.txt"; - cals::Tensor T(file_name); - compare_als_cals(T, ranks, num_threads, params); - } - - // Real Dataset (Fluorescence Cancer) comparison of ALS and CALS - if (true) - { - vector ranks; - generate_ranks(ranks, 1, 20, 20); - std::sort(ranks.begin(), ranks.end()); - - cals::CalsParams params; - params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - params.max_iterations = 50; // TTB default - params.buffer_size = 4200; - params.tol = 1e-4; // TTB default - params.cuda = false; - - std::string file_name = "../data/fluorescence_cancer_UD.txt"; - cals::Tensor T(file_name); - compare_als_cals(T, ranks, num_threads, params); - } - - // Synthetic Dataset comparison of ALS and CALS - if (true) - { - vector ranks; - generate_ranks(ranks, 1, 20, 20); - std::sort(ranks.begin(), ranks.end()); - - cals::CalsParams params; - params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - params.force_max_iter = true; - params.max_iterations = 50; - params.tol = 1e-4; - params.cuda = false; - - if (num_threads == 1) - params.buffer_size = 90; - else - params.buffer_size = 4200; - compare_als_cals(25, {100, 100, 100}, ranks, num_threads, params); - params.buffer_size = 4200; - compare_als_cals(25, {200, 200, 200}, ranks, num_threads, params); - compare_als_cals(25, {300, 300, 300}, ranks, num_threads, params); - } - - // Speedup plot ALS v CALS - if (true) - { - cals::CalsParams params; - params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - params.force_max_iter = true; - params.max_iterations = 50; - params.tol = 1e-4; - params.cuda = false; - - for (int rank = 1; rank <= 20; rank++) - { - vector ranks; - generate_ranks(ranks, rank, rank, 20); - - if (num_threads == 1) - params.buffer_size = 90; - else - params.buffer_size = 20 * rank; - compare_als_cals(25, {100, 100, 100}, ranks, num_threads, params, "speedup_" + std::to_string(rank)); - params.buffer_size = 20 * rank; - compare_als_cals(25, {200, 200, 200}, ranks, num_threads, params, "speedup_" + std::to_string(rank)); - compare_als_cals(25, {300, 300, 300}, ranks, num_threads, params, "speedup_" + std::to_string(rank)); - } - } - - // MTTKRP Benchmark for sweeping intensity - if (true) - { - if (true) - { - auto params = cals::ops::MttkrpParams{}; - params.method = cals::ops::MTTKRP_METHOD::MTTKRP; - cals::benchmarker::benchmark_mttkrp({100, 100, 100}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({200, 200, 200}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({300, 300, 300}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({299, 301, 41}, num_threads, params); - } - if (true) - { - auto params = cals::ops::MttkrpParams{}; - params.method = cals::ops::MTTKRP_METHOD::TWOSTEP0; - cals::benchmarker::benchmark_mttkrp({100, 100, 100}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({200, 200, 200}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({300, 300, 300}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({299, 301, 41}, num_threads, params); - } - if (true) - { - auto params = cals::ops::MttkrpParams{}; - params.method = cals::ops::MTTKRP_METHOD::TWOSTEP1; - cals::benchmarker::benchmark_mttkrp({100, 100, 100}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({200, 200, 200}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({300, 300, 300}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({299, 301, 41}, num_threads, params); - } - } -} - -void generate_ranks(vector &ranks, int min, int max, int copies) -{ - for (auto rank = min; rank <= max; rank++) - for (auto cp = 0; cp < copies; cp++) - ranks.push_back(rank); -} diff --git a/src/experiments/cuda_experiment.cpp b/src/experiments/cuda_experiment.cpp deleted file mode 100644 index 7e32ff0..0000000 --- a/src/experiments/cuda_experiment.cpp +++ /dev/null @@ -1,143 +0,0 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - -#include -#include -#include - -#include "experiments/experiments_utils.h" -#include "experiments/mttkrp_benchmarker.h" - -void generate_ranks(vector &ranks, int min, int max, int copies); - -int main(int argc, char *argv[]) -{ - if (argc != 2) - { - std::cerr << "Not enough arguments. Give number of threads."; - abort(); - } - - int num_threads = std::atoi(argv[1]); // NOLINT(cert-err34-c) - - // Mock run to warmup everything. - if (true) - { - vector ranks; - generate_ranks(ranks, 1, 20, 20); - std::sort(ranks.begin(), ranks.end()); - - cals::CalsParams params; - params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - params.max_iterations = 50; // TTB default - params.buffer_size = 4200; - params.tol = 1e-4; // TTB default - params.cuda = true; - - std::string file_name = "../data/fluorescence_cancer_UD.txt"; - cals::Tensor T(file_name); - run_cals(T, ranks, num_threads, params, true); - } - - // Real Dataset (Fluorescence Cancer) comparison of ALS and CALS - if (true) - { - vector ranks; - generate_ranks(ranks, 1, 20, 20); - std::sort(ranks.begin(), ranks.end()); - - cals::CalsParams params; - params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - params.max_iterations = 50; // TTB default - params.buffer_size = 4200; - params.tol = 1e-4; // TTB default - params.cuda = true; - - std::string file_name = "../data/fluorescence_cancer_UD.txt"; - cals::Tensor T(file_name); - run_cals(T, ranks, num_threads, params, true); - } - - // Synthetic Dataset comparison of ALS and CALS - if (true) - { - vector ranks; - generate_ranks(ranks, 1, 20, 20); - std::sort(ranks.begin(), ranks.end()); - - cals::CalsParams params; - params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - params.force_max_iter = true; - params.max_iterations = 50; - params.buffer_size = 4200; - params.tol = 1e-4; - params.cuda = true; - - run_cals(25, {100, 100, 100}, ranks, num_threads, params, true); - run_cals(25, {200, 200, 200}, ranks, num_threads, params, true); - run_cals(25, {300, 300, 300}, ranks, num_threads, params, true); - } - - // Speedup plot ALS v CALS v CUDA - if (true) - { - cals::CalsParams params; - params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; - params.force_max_iter = true; - params.max_iterations = 50; - params.tol = 1e-4; - params.cuda = true; - - for (int rank = 1; rank <= 20; rank++) - { - vector ranks; - generate_ranks(ranks, rank, rank, 20); - - params.buffer_size = 20 * rank; - run_cals(25, {100, 100, 100}, ranks, num_threads, params, true, "speedup_" + std::to_string(rank)); - run_cals(25, {200, 200, 200}, ranks, num_threads, params, true, "speedup_" + std::to_string(rank)); - run_cals(25, {300, 300, 300}, ranks, num_threads, params, true, "speedup_" + std::to_string(rank)); - } - } -} - -void generate_ranks(vector &ranks, int min, int max, int copies) -{ - for (auto rank = min; rank <= max; rank++) - for (auto cp = 0; cp < copies; cp++) - ranks.push_back(rank); -} diff --git a/src/experiments/experiments.cpp b/src/experiments/experiments.cpp new file mode 100644 index 0000000..1d86127 --- /dev/null +++ b/src/experiments/experiments.cpp @@ -0,0 +1,151 @@ +#include "experiments/experiments_utils.h" + +using std::cerr; +using std::cout; +using std::endl; +using std::string; + +int main(int argc, char *argv[]) { + cout << "=============================================================================" << endl; + cout << "This executable performs the experiments in the paper." << endl; + cout << "The corresponding source file is located in `src/experiments/experiments.cpp`" << endl; + cout << "The output is writen in CSV files in the `data` folder of the project." << endl; + cout << "This executable accepts only one (mandatory) argument, the number of threads." << endl; + cout << "=============================================================================" << endl; + cout << endl; + + if (argc != 2) { + cerr << "Not enough arguments. Give number of threads." << endl; + cout << "USAGE: " << argv[0] << " " << endl; + abort(); + } else { + std::string arg = argv[1]; + if ((arg == "-h") || (arg == "--help")) { + cout << "USAGE: " << argv[0] << " " << endl; + return 0; + } + } + + auto num_threads = static_cast(std::strtoul(argv[1], nullptr, 10)); + + bool experiment_6_1_1 = true; + bool experiment_6_1_2_and_6_3 = true; + bool experiment_6_2 = true; + + // Mock run to warmup everything. + { + auto components = generate_components(1, 2, 2); + std::sort(components.begin(), components.end()); + + cals::CalsParams params; + params.mttkrp_method = cals::mttkrp::MTTKRP_METHOD::AUTO; + params.max_iterations = 5; + params.buffer_size = 10; + params.tol = 1e-4; +#if CUDA_ENABLED + params.cuda = true; +#else + params.cuda = false; +#endif + + vector modes = {20, 20, 20}; + cals::Tensor T(modes); + T.randomize(); + params.mttkrp_lut = cals::mttkrp::read_lookup_table({100, 100, 100}, num_threads, params.cuda); + compare_als_cals(T, components, num_threads, params); + } + + if (experiment_6_1_1) { + // Set the parameters of the execution + cals::CalsParams params; + params.mttkrp_method = cals::mttkrp::MTTKRP_METHOD::AUTO; // Let the Lookup tables decide the best method. + params.force_max_iter = true; // Force all models to reach max_iterations. + params.max_iterations = 50; // Set maximum number of iterations. +#if CUDA_ENABLED + params.cuda = true; +#else + params.cuda = false; +#endif + + for (int comp = 1; comp <= 20; comp++) { + auto components = generate_components(comp, comp, 20); // Generate 20 models for every number of components + + vector> modes_v; + modes_v.push_back({100, 100, 100}); + modes_v.push_back({200, 200, 200}); + modes_v.push_back({300, 300, 300}); + + for (auto &modes : modes_v) { + if (num_threads == 1 && modes[0] == 100 && modes[1] == 100 && modes[2] == 100) + params.buffer_size = 90; // Selecting the optimal buffer size for MTTKRP (as explained in the paper) + else + params.buffer_size = static_cast(20) * comp; // Else, select the largest buffer size to fit all models + int nt = (num_threads == 1) ? 1 : 24; + params.mttkrp_lut = cals::mttkrp::read_lookup_table(modes, nt, params.cuda); + cals::Tensor T(25, modes); + T.randomize(); + compare_als_cals(T, components, num_threads, params, "speedup_" + std::to_string(comp)); + } + } + } + + if (experiment_6_1_2_and_6_3) { + auto components = generate_components(1, 20, 20); + std::sort(components.begin(), components.end()); + + // Set the parameters of the execution + cals::CalsParams params; + params.mttkrp_method = cals::mttkrp::MTTKRP_METHOD::AUTO; // Let the Lookup tables decide the best method. + params.force_max_iter = true; // Force all models to reach max_iterations. + params.max_iterations = 50; // Set maximum number of iterations. +#if CUDA_ENABLED + params.cuda = true; +#else + params.cuda = false; +#endif + + vector> modes_v; + modes_v.push_back({100, 100, 100}); + modes_v.push_back({200, 200, 200}); + modes_v.push_back({300, 300, 300}); + + for (auto &modes : modes_v) { + if (num_threads == 1 && modes[0] == 100 && modes[1] == 100 && modes[2] == 100) + params.buffer_size = 90; // Selecting the optimal buffer size for MTTKRP (as explained in the paper) + else + params.buffer_size = 4200; // Else, select the largest buffer size to fit all models + int nt = (num_threads == 1) ? 1 : 24; + params.mttkrp_lut = cals::mttkrp::read_lookup_table(modes, nt, params.cuda); + cals::Tensor T(modes); + T.randomize(); + compare_als_cals(T, components, num_threads, params); + } + } + + if (experiment_6_2) { + auto components = generate_components(1, 20, 20); + std::sort(components.begin(), components.end()); + + cals::CalsParams params; + params.mttkrp_method = cals::mttkrp::MTTKRP_METHOD::AUTO; + params.max_iterations = 1000; // Set the maximum number of iterations. + params.buffer_size = 4200; // Set the buffer size to the sum of all number of components. + params.tol = 1e-6; // Set the tolerance. +#if CUDA_ENABLED + params.cuda = true; +#else + params.cuda = false; +#endif + + string file_name = string(SOURCE_DIR) + "/data/fluorescence_cancer_UD.txt"; + cals::Tensor T_cancer(file_name); + int nt = (num_threads == 1) ? 1 : 24; + params.mttkrp_lut = cals::mttkrp::read_lookup_table({299, 301, 41}, nt, params.cuda); + compare_als_cals(T_cancer, components, num_threads, params, "", true); + + file_name = string(SOURCE_DIR) + "/data/eemdata.txt"; + cals::Tensor T_eemdata(file_name); + params.mttkrp_lut = cals::mttkrp::read_lookup_table({405, 136, 19}, nt, params.cuda); + compare_als_cals(T_eemdata, components, num_threads, params, "", true); + } +} diff --git a/src/experiments/experiments_letter.cpp b/src/experiments/experiments_letter.cpp new file mode 100644 index 0000000..bd2cbc8 --- /dev/null +++ b/src/experiments/experiments_letter.cpp @@ -0,0 +1,52 @@ +#include "experiments/experiments_utils.h" + +using std::cerr; +using std::cout; +using std::endl; +using std::string; + +int main(int argc, char *argv[]) { + cout << "=============================================================================" << endl; + cout << "This executable performs experiments related to the letter." << endl; + cout << "The corresponding source file is located in `src/experiments/experiments_letter.cpp`" << endl; + cout << "The output is writen in CSV files in the `data` folder of the project." << endl; + cout << "This executable accepts only one (mandatory) argument, the number of threads." << endl; + cout << "=============================================================================" << endl; + cout << endl; + + if (argc != 2) { + cerr << "Not enough arguments. Give number of threads." << endl; + cout << "USAGE: " << argv[0] << " " << endl; + abort(); + } else { + std::string arg = argv[1]; + if ((arg == "-h") || (arg == "--help")) { + cout << "USAGE: " << argv[0] << " " << endl; + return 0; + } + } + + auto num_threads = static_cast(std::strtol(argv[1], nullptr, 10)); + + bool exp_defrag = true; + + if (exp_defrag) { + vector modes = {200, 200, 200}; + cals::Tensor T(modes); + T.randomize(); + + auto ranks = generate_components(1, 20, 20); + + cals::CalsParams params; + params.mttkrp_method = cals::mttkrp::MTTKRP_METHOD::AUTO; + params.max_iterations = 1000; + params.always_evict_first = true; +#if CUDA_ENABLED + params.cuda = true; +#else + params.cuda = false; +#endif + + run_cals(T, ranks, num_threads, params, true, "defrag"); + } +} \ No newline at end of file diff --git a/src/experiments/experiments_utils.cpp b/src/experiments/experiments_utils.cpp index b6fd049..1646156 100644 --- a/src/experiments/experiments_utils.cpp +++ b/src/experiments/experiments_utils.cpp @@ -1,238 +1,242 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #include "experiments/experiments_utils.h" #include -#include #include -#include +#include #include "als.h" -#include "cals.h" -#include "utils.h" -#include "cals_blas.h" using cals::Ktensor; using cals::Tensor; -using std::vector; using std::cout; using std::endl; +using std::string; +using std::vector; +vector generate_components(int min, int max, int copies) { + vector components; + for (auto comp = min; comp <= max; comp++) + for (auto cp = 0; cp < copies; cp++) + components.push_back(comp); + return components; +} -vector regular_als(const Tensor &X, cals::KtensorQueue &kt_queue, cals::CalsParams ¶ms) -{ - vector reports(kt_queue.size()); +vector +regular_als(const Tensor &X, vector &ktensor_v, cals::CalsParams ¶ms, bool openmp_f = false) { + vector reports(ktensor_v.size()); cals::AlsParams als_params; + als_params.update_method = params.update_method; als_params.mttkrp_method = params.mttkrp_method; + als_params.mttkrp_lut = params.mttkrp_lut; als_params.max_iterations = params.max_iterations; als_params.force_max_iter = params.force_max_iter; - - // Sweep over Ktensor models. - int i = 0; - cout << "Finished ALS:"; - while (!kt_queue.empty()) - { - reports[i++] = cals::cp_als(X, kt_queue.front(), als_params); - cout << " " << i; - kt_queue.pop(); + als_params.tol = params.tol; + als_params.line_search = params.line_search; + als_params.line_search_interval = params.line_search_interval; + als_params.line_search_step = params.line_search_step; + als_params.cuda = params.cuda; + + cals::Timer als_time; + als_time.start(); + if (openmp_f) { + // cout << "MKL_DYNAMIC: " << mkl_get_dynamic() << endl; + // cout << "OMP_NUM_THREADS: " << omp_get_max_threads() << endl; + reports = cals::cp_omp_als(X, ktensor_v, als_params); + } else { + for (auto i = 0lu; i < ktensor_v.size(); i++) + reports[i] = cals::cp_als(X, ktensor_v[i], als_params); } - cout << endl; + als_time.stop(); + std::cout << ((openmp_f) ? "O" : " ") << "ALS Computation time: " << als_time.get_time() << std::endl; return reports; } -cals::CalsReport concurrent_als(const Tensor &X, cals::KtensorQueue &kt_queue, cals::CalsParams ¶ms) -{ - // - // Configuration - // +cals::CalsReport concurrent_als(const Tensor &X, cals::KtensorQueue &kt_queue, cals::CalsParams ¶ms) { + cals::Timer cals_time; + cals_time.start(); - // Fit the models using Simultaneous ALS. - return cals::cp_cals(X, kt_queue, params); -} + // Fit the models using Concurrent ALS (CALS). + auto report = cals::cp_cals(X, kt_queue, params); + + cals_time.stop(); + std::cout << "CALS Computation time: " << cals_time.get_time() << std::endl; -void compare_als_cals(int tensor_rank, const vector &modes, vector &ranks, int num_threads, - cals::CalsParams ¶ms, const std::basic_string& file_suffix) -{ - Tensor X(tensor_rank, modes); - compare_als_cals(X, ranks, num_threads, params, file_suffix); + return report; } -void compare_als_cals(const Tensor &X, vector &ranks, int num_threads, cals::CalsParams ¶ms, - const std::basic_string& file_suffix) -{ +void compare_als_cals(const Tensor &X, + vector &components, + unsigned int num_threads, + cals::CalsParams ¶ms, + const std::basic_string &file_suffix, + bool reproducible) { + cout << "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++" << endl; + cout << "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++" << endl; + cout << "START ALS v OMP ALS v CALS" << endl; + + std::mt19937 reproducible_generator(0); + //////////////////////////////////////////////////////////////////////// + // Create Tensor, Ktensors and run experiments + //////////////////////////////////////////////////////////////////////// set_threads(num_threads); + cout << "Number of threads set: " << get_threads() << endl; auto const modes = X.get_modes(); - cout << "Modes: "; - for (auto m : modes) cout << m << " "; - cout << endl; + auto const n_ktensors = components.size(); + cout << "Modes: " << cals::utils::mode_string(modes) << endl; - vector ktensor_vector(ranks.size()); + vector ktensor_vector(n_ktensors); auto i = 0; - for (auto &ktensor : ktensor_vector) - { - ktensor = Ktensor(ranks[i++], modes); - ktensor.randomize(); + for (auto &ktensor : ktensor_vector) { + ktensor = Ktensor(components[i++], modes); + if (reproducible) { + std::uniform_real_distribution dist(-1.0, 1.0); + ktensor.fill( + (function &&)[&dist, &reproducible_generator]()->double { return dist(reproducible_generator); }); + } else + ktensor.randomize(); } - auto als_input(ktensor_vector); - auto cals_input(ktensor_vector); - - cals::KtensorQueue als_queue, cals_queue; - for (auto p = 0lu; p < als_input.size(); p++) - { - als_queue.emplace(als_input[p]); + cout << "----------------------" << endl; + cout << "Starting CALS" << endl; + auto cals_input(ktensor_vector); + cals::KtensorQueue cals_queue; + for (auto p = 0lu; p < n_ktensors; p++) cals_queue.emplace(cals_input[p]); + auto cals_report = concurrent_als(X, cals_queue, params); + + auto als_omp_report = vector(); + auto als_omp_input(ktensor_vector); + if (num_threads != 1) { + cout << "----------------------" << endl; + cout << "Starting OMP ALS" << endl; + auto enable_omp = true; + als_omp_report = regular_als(X, als_omp_input, params, enable_omp); } - cout << "Starting CALS" << endl; - auto cals_report = concurrent_als(X, cals_queue, params); + cout << "----------------------" << endl; cout << "Starting ALS" << endl; - auto als_report = regular_als(X, als_queue, params); + auto als_input(ktensor_vector); + auto enable_omp = false; + auto als_report = regular_als(X, als_input, params, enable_omp); - auto index = 0; - auto const xnorm = X.norm(); - for (auto &k : cals_input) - { - auto model_norm_diff_pct = (als_input[index++].get_approx_error() - k.get_approx_error()) / xnorm; - - if (std::isnan(model_norm_diff_pct) - || std::isnan(k.get_factor(0)[0]) - || std::isnan(k.get_factor(1)[0]) - || std::isnan(k.get_factor(2)[0])) - { + //////////////////////////////////////////////////////////////////////// + // Check whether results are the same across all 3 methods + //////////////////////////////////////////////////////////////////////// + for (auto p = 0lu; p < n_ktensors; p++) { + auto model_norm_diff = std::fabs(als_input[p].get_approximation_error() - cals_input[p].get_approximation_error()); + + if (std::isnan(model_norm_diff) || std::isnan(cals_input[p].get_factor(0)[0]) || + std::isnan(cals_input[p].get_factor(1)[0]) || std::isnan(cals_input[p].get_factor(2)[0])) std::cerr << "NaN values in solutions" << std::endl; - abort(); - } - if (std::fabs(model_norm_diff_pct) > MODEL_DIFF_ACC) - { - std::cerr << "Ktensor " << index - 1 << " ALS v CALS: " << model_norm_diff_pct * 100 << " %" << endl; -// abort(); + if (model_norm_diff > MODEL_DIFF_ACC) + std::cerr << std::scientific << "Ktensor: " << p << " ALS v CALS: " << std::setw(9) << model_norm_diff + << " ALS: " << std::setw(9) << als_input[p].get_approximation_error() << " CALS: " << std::setw(9) + << cals_input[p].get_approximation_error() << endl; + + if (num_threads != 1) { + auto model_norm_diff_1 = + std::fabs(als_omp_input[p].get_approximation_error() - cals_input[p].get_approximation_error()); + if (std::isnan(model_norm_diff_1)) + std::cerr << "NaN values in solutions" << std::endl; + if (model_norm_diff_1 > MODEL_DIFF_ACC) + std::cerr << std::scientific << "Ktensor: " << p << " OALS v CALS: " << std::setw(9) << model_norm_diff_1 + << " OALS: " << std::setw(9) << als_omp_input[p].get_approximation_error() + << " CALS: " << std::setw(9) << cals_input[p].get_approximation_error() << endl; } } + //////////////////////////////////////////////////////////////////////// // Print results to file - std::string str_mode; - for (const auto &m : modes) str_mode += std::to_string(m) + "-"; - str_mode.pop_back(); - std::string str_n_threads = std::to_string(cals_report.n_threads); - std::string str_blas_vendor = CALS_BACKEND; - std::string dir = "../data/" + str_blas_vendor + "/"; - std::string suffix; - if (!file_suffix.empty()) suffix = "_" + file_suffix; - - std::string als_file_name = dir + "ALS_" + str_blas_vendor + "_" + str_mode + "_" + str_n_threads + suffix + ".csv"; + //////////////////////////////////////////////////////////////////////// + string str_mode = cals::utils::mode_string(modes); + string str_n_threads = std::to_string(cals_report.n_threads); + string str_blas_vendor = CALS_BACKEND; + string str_cuda = (params.cuda) ? "CUDA_" : ""; + string dir = string(SOURCE_DIR) + "/data/" + str_blas_vendor + "/"; + string suffix = ".csv"; + if (!file_suffix.empty()) + suffix = "_" + file_suffix + ".csv"; + + string als_file_name = dir + "ALS_" + str_cuda + str_blas_vendor + "_" + str_mode + "_" + str_n_threads + suffix; als_report[0].print_header(als_file_name); - for (auto &rm : als_report) rm.print_to_file(als_file_name); + for (auto &rm : als_report) + rm.print_to_file(als_file_name); + + if (num_threads != 1) { + string als_omp_file_name = + dir + "ALS_" + "OMP_" + str_cuda + str_blas_vendor + "_" + str_mode + "_" + str_n_threads + suffix; + als_report[0].print_header(als_omp_file_name); + for (auto &rm : als_omp_report) + rm.print_to_file(als_omp_file_name); + } - std::string cals_file_name = dir + "CALS_" + str_blas_vendor + "_" + str_mode + "_" + str_n_threads + suffix + ".csv"; + string cals_file_name = dir + "CALS_" + str_cuda + str_blas_vendor + "_" + str_mode + "_" + str_n_threads + suffix; cals_report.print_header(cals_file_name); cals_report.print_to_file(cals_file_name); - // Print all Ktensor ids, ranks, errors and iterations - std::string ktensor_file_name = dir + "Ktensors_" + str_blas_vendor + "_" + str_mode + "_" + str_n_threads + suffix + ".csv"; - - auto file = std::ofstream(ktensor_file_name, std::ios::app); + // Print all Ktensor ids, components, errors and iterations + string ktensor_file_name = + dir + "Ktensors_" + str_cuda + str_blas_vendor + "_" + str_mode + "_" + str_n_threads + suffix; + auto file = std::ofstream(ktensor_file_name, std::ios::out); file << "KTENSOR_ID;KTENSOR_RANK;ERROR;ITERS" << endl; for (auto &k : cals_input) - file << k.get_id() << ";" << k.get_rank() << ";" << k.get_approx_error() << ";" << k.get_iters() << endl; -} + file << k.get_id() << ";" << k.get_components() << ";" << k.get_approximation_error() << ";" << k.get_iters() + << endl; -void run_cals(int tensor_rank, const vector &modes, vector &ranks, int num_threads, - cals::CalsParams ¶ms, bool print_header, const std::basic_string& file_suffix) -{ - Tensor X(tensor_rank, modes); - run_cals(X, ranks, num_threads, params, print_header, file_suffix); + cout << endl; + cout << "STOP ALS v OMP ALS v CALS" << endl; + cout << "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++" << endl; + cout << endl; } -void run_cals(const Tensor &X, vector &ranks, int num_threads, cals::CalsParams ¶ms, bool print_header, - const std::basic_string& file_suffix) -{ +void run_cals(const Tensor &X, + vector &components, + unsigned int num_threads, + cals::CalsParams ¶ms, + bool print_header, + const std::basic_string &file_suffix) { + cout << "============================" << endl; + cout << "START Single CALS call" << endl; + set_threads(num_threads); + cout << "Threads: " << get_threads() << endl; - cout << "============================" << endl; - cout << "START Single CALS experiment" << endl; - cout << "Modes: "; auto const modes = X.get_modes(); - for (auto m : modes) cout << m << " "; - cout << endl; - cout << "Threads: " << num_threads << endl; + cout << "Modes: " << cals::utils::mode_string(modes) << endl; - vector ktensor_vector(ranks.size()); + vector ktensor_vector(components.size()); auto i = 0; - for (auto &ktensor : ktensor_vector) - { - ktensor = Ktensor(ranks[i++], modes); + for (auto &ktensor : ktensor_vector) { + ktensor = Ktensor(components[i++], modes); ktensor.randomize(); } auto cals_input(ktensor_vector); cals::KtensorQueue cals_queue; - - for (auto & p : cals_input) cals_queue.emplace(p); - + for (auto &p : cals_input) + cals_queue.emplace(p); auto cals_report = concurrent_als(X, cals_queue, params); // Print results to file - std::string str_mode; - for (const auto &m : modes) str_mode += std::to_string(m) + "-"; - str_mode.pop_back(); - std::string str_n_threads = std::to_string(cals_report.n_threads); - std::string str_blas_vendor = CALS_BACKEND; - std::string dir = "../data/" + str_blas_vendor + "/"; - std::string str_cuda = (params.cuda) ? "CUDA_" : ""; - - std::string cals_file_name = dir + "CALS_" + str_cuda + str_blas_vendor + "_" + str_mode + "_" + str_n_threads; - if (file_suffix.empty()) - cals_file_name += ".csv"; - else - cals_file_name += "_" + file_suffix + ".csv"; + string str_mode = cals::utils::mode_string(modes); + string str_n_threads = std::to_string(cals_report.n_threads); + string str_blas_vendor = CALS_BACKEND; + string dir = string(SOURCE_DIR) + "/data/" + str_blas_vendor + "/"; + string str_cuda = (params.cuda) ? "CUDA_" : ""; + string suffix = ".csv"; + if (!file_suffix.empty()) + suffix = "_" + file_suffix + ".csv"; + string cals_file_name = dir + "CALS_" + str_cuda + str_blas_vendor + "_" + str_mode + "_" + str_n_threads + suffix; cals_report.output_file_name = cals_file_name; - if (print_header) cals_report.print_header(cals_file_name); cals_report.print_to_file(cals_file_name); cout << cals_report.output_file_name << endl; - cout << "END Single CALS experiment" << endl; + cout << "END Single CALS call" << endl; cout << "==========================" << endl; - } diff --git a/src/experiments/mttkrp_benchmarker.cpp b/src/experiments/mttkrp_benchmarker.cpp deleted file mode 100644 index 75604ed..0000000 --- a/src/experiments/mttkrp_benchmarker.cpp +++ /dev/null @@ -1,134 +0,0 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - -#include "experiments/mttkrp_benchmarker.h" - -#include -#include -#include -#include - -#include "cals_blas.h" -#include "timer.h" - -using std::string; -using std::to_string; -using std::ofstream; -using std::cout; -using std::endl; - -namespace cals::benchmarker -{ - void initialize_csv(string &file_name, const string &sep = ";") - { - auto file = ofstream(file_name, std::ios::out); - file << "MODE" << sep - << "TENSOR_MODES" << sep - << "FLOPS" << sep - << "MEMOPS" << sep - << "RANK" << sep - << "THREADS" << sep - << "TIME" << endl; - } - - void append_csv(string &file_name, int mode, string &modes_string, int rank, int threads, - uint64_t flops, uint64_t memops, double time, const string &sep = ";") - { - auto file = std::ofstream(file_name, std::ios::app); - file << mode << sep - << modes_string << sep - << flops << sep - << memops << sep - << rank << sep - << threads << sep - << time << endl; - } - - void benchmark_mttkrp(const vector &modes, int num_threads, cals::ops::MttkrpParams ¶ms) - { - Tensor T(modes); - - set_threads(num_threads); - cout << "Threads: " << get_threads() << endl; - - //////////////////////////////////////////////////////////////////////////////////// - // Create CSV file name - //////////////////////////////////////////////////////////////////////////////////// - string folder = "../data/" + string(CALS_BACKEND) + "/benchmark/"; - - string modes_string; - for (auto m : modes) modes_string += to_string(m) + "-"; - modes_string.pop_back(); - - string file_name; - file_name = folder - + "benchmark_" - + string(CALS_BACKEND) + "_" - + modes_string + "_" - + to_string(get_threads()) + "_" - + to_string(params.method) - + ".csv"; - - initialize_csv(file_name); - cout << "File name: " << file_name << endl; - - //////////////////////////////////////////////////////////////////////////////////// - // Calculate target ranks - //////////////////////////////////////////////////////////////////////////////////// - vector ranks; - ranks.reserve(300); - for (auto i = 1; i < 20; i += 1) ranks.push_back(i); - for (auto i = 20; i < 100; i += 10) ranks.push_back(i); - for (auto i = 100; i < 1000; i += 100) ranks.push_back(i); - for (auto i = 1000; i <= 5000; i += 1000) ranks.push_back(i); - - for (auto m = 0; m < T.get_n_modes(); m++) - { - for (auto rank : ranks) - { - Timer gemm_t, krp_t, ts_gemm_t, ts_gemv_t; - params.timer_mttkrp_gemm = &gemm_t; - params.timer_mttkrp_krp = &krp_t; - params.timer_twostep_gemm = &ts_gemm_t; - params.timer_twostep_gemv = &ts_gemv_t; - - auto min_time = benchmark(rank, m, T, cals::ops::mttkrp, params); - append_csv(file_name, m, modes_string, rank, num_threads, params.flops, params.memops, min_time); - } - } - } -} diff --git a/src/experiments/mttkrp_benchmarks.cpp b/src/experiments/mttkrp_benchmarks.cpp deleted file mode 100644 index 2d838c4..0000000 --- a/src/experiments/mttkrp_benchmarks.cpp +++ /dev/null @@ -1,82 +0,0 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - -#include -#include -#include - -#include "experiments/mttkrp_benchmarker.h" - -int main(int argc, char *argv[]) -{ - if (argc != 2) - { - std::cerr << "Not enough arguments. Give number of threads."; - abort(); - } - - int num_threads = std::atoi(argv[1]); // NOLINT(cert-err34-c) - - // MTTKRP Benchmark for sweeping intensity - if (true) - { - if (true) - { - auto params = cals::ops::MttkrpParams{}; - params.method = cals::ops::MTTKRP_METHOD::MTTKRP; - cals::benchmarker::benchmark_mttkrp({100, 100, 100}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({200, 200, 200}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({300, 300, 300}, num_threads, params); - } - if (true) - { - auto params = cals::ops::MttkrpParams{}; - params.method = cals::ops::MTTKRP_METHOD::TWOSTEP0; - cals::benchmarker::benchmark_mttkrp({100, 100, 100}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({200, 200, 200}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({300, 300, 300}, num_threads, params); - } - if (true) - { - auto params = cals::ops::MttkrpParams{}; - params.method = cals::ops::MTTKRP_METHOD::TWOSTEP1; - cals::benchmarker::benchmark_mttkrp({100, 100, 100}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({200, 200, 200}, num_threads, params); - cals::benchmarker::benchmark_mttkrp({300, 300, 300}, num_threads, params); - } - } -} diff --git a/src/experiments/peak_evaluator.cpp b/src/experiments/peak_evaluator.cpp index 259314a..e318630 100644 --- a/src/experiments/peak_evaluator.cpp +++ b/src/experiments/peak_evaluator.cpp @@ -1,79 +1,57 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #include -#include #include +#include #include "matrix.h" #include "timer.h" -#include "cals_blas.h" -const int iterations = 10; +const int iterations = 100; +using std::cerr; using std::cout; using std::endl; -using std::cerr; -int main(int argc, char *argv[]) -{ - if (argc != 5) - { - cerr << "Not enough arguments." << endl; - cerr << "Give number of threads, AVX peak freq (in MHz) for those threads, Flops/Cycle and size of GEMM." - << endl; - cerr << "Example: ./Evaluator 24 2000 32 1000" - << endl - << "for 24 threads, 2000 MHz AVX peak freq for 24 threads, 32 Flops/Cycle and matrix sizes 1000x1000." - << endl; +int main(int argc, char *argv[]) { + + cout << "=============================================================================" << endl; + cout << "This executable helps create the GEMM lines in figure 5." << endl; + cout << "The corresponding source file is located in `src/experiments/peak_evaluator.cpp`" << endl; + cout << "The output is writen in standard output." << endl; + cout << "=============================================================================" << endl; + + cout << "USAGE: " << argv[0] << " 24 2000 32 1000" << endl + << "for 24 threads, 2000 MHz AVX peak freq for 24 threads, 32 Flops/Cycle and matrix sizes 1000x1000." << endl; + cout << endl; + + if (argc != 5) { + cerr << "Not enough arguments!" << endl; + cerr << "Give number of threads, AVX peak freq (in MHz) for those threads, Flops/Cycle and size of GEMM." << endl; + cerr << "Example: " << argv[0] << " 24 2000 32 1000" << endl + << "for 24 threads, 2000 MHz AVX peak freq for 24 threads, 32 Flops/Cycle and matrix sizes 1000x1000." << endl; abort(); } + for (int i = 1; i < argc; ++i) { + std::string arg = argv[i]; + if ((arg == "-h") || (arg == "--help")) { + cout << "Give number of threads, AVX peak freq (in MHz) for those threads, Flops/Cycle and size of GEMM." << endl; + cout << "Example: " << argv[0] << " 24 2000 32 1000" << endl + << "for 24 threads, 2000 MHz AVX peak freq for 24 threads, 32 Flops/Cycle and matrix sizes 1000x1000." + << endl; + return 0; + } + } typedef long long unsigned int lint; - const lint num_threads = std::atoi(argv[1]); // NOLINT(cert-err34-c) - set_threads(num_threads); - lint freq = std::atoi(argv[2]); // NOLINT(cert-err34-c) + const lint num_threads = std::strtoull(argv[1], nullptr, 10); + set_threads((int)num_threads); + lint freq = std::strtoull(argv[2], nullptr, 10); freq *= 1000; freq *= 1000; - const lint fpc = std::atoi(argv[3]); // NOLINT(cert-err34-c) + const lint fpc = std::strtoull(argv[3], nullptr, 10); const lint tpp = num_threads * freq * fpc; - lint size = std::atoi(argv[4]); // NOLINT(cert-err34-c) + lint size = std::strtoull(argv[4], nullptr, 10); cals::Matrix A(size, size), B(size, size), C(size, size); cals::Matrix cache(3000, 3000); vector vec_t(iterations, cals::Timer()); @@ -83,14 +61,12 @@ int main(int argc, char *argv[]) C.randomize(); const lint flops = 2llu * A.get_rows() * A.get_cols() * B.get_cols() + 2llu * C.get_rows() * C.get_cols(); - for (int i = 0; i < iterations; i++) - { + for (int i = 0; i < iterations; i++) { cache.randomize(); auto &t = vec_t[i]; t.start(); - cblas_dgemm(CblasColMajor, CblasNoTrans, CblasNoTrans, A.get_rows(), B.get_cols(), A.get_cols(), - 1.0, A.get_data(), A.get_col_stride(), B.get_data(), B.get_col_stride(), - 0.0, C.get_data(), C.get_col_stride()); + cblas_dgemm(CblasColMajor, CblasNoTrans, CblasNoTrans, A.get_rows(), B.get_cols(), A.get_cols(), 1.0, A.get_data(), + A.get_col_stride(), B.get_data(), B.get_col_stride(), 0.0, C.get_data(), C.get_col_stride()); t.stop(); } diff --git a/src/ktensor.cpp b/src/ktensor.cpp index 95b75de..eaeeae5 100644 --- a/src/ktensor.cpp +++ b/src/ktensor.cpp @@ -1,224 +1,180 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #include "ktensor.h" -#include #include +#include #if CUDA_ENABLED #include "cuda_utils.h" #endif -namespace cals -{ - Ktensor &Ktensor::randomize() - { - for (auto &f : factors) f.randomize(); - normalize(); - return *this; - } - - void Ktensor::rec_to_tensor(vector &modes, int &index, double *const x_data, int const level, int *const dim_ind) - { - if (level >= 1) - for (auto i = 0; i < modes[level - 1]; i++) - { - dim_ind[level - 1] = i; - rec_to_tensor(modes, index, x_data, level - 1, dim_ind); - } - else if (level == 0) - { - double s = 0; - for (auto r = 0; r < rank; r++) - { - double m = 1.0; - for (auto f = 0; f < get_n_modes(); f++) - m *= factors[f].at(dim_ind[f], r); - s += lambda[r] * m; - } - x_data[index++] = s; +namespace cals { +Ktensor &Ktensor::randomize() { + for (auto &f : factors) + f.randomize(); + normalize(); + return *this; +} + +Ktensor &Ktensor::fill(function &&func) { + for (auto &f : factors) + f.fill(std::forward(func)); + normalize(); + return *this; +} + +void Ktensor::rec_to_tensor( + vector &modes, int &index, double *const x_data, int const level, dim_t *const dim_ind) { + if (level >= 1) + for (dim_t i = 0; i < modes[level - 1]; i++) { + dim_ind[level - 1] = i; + rec_to_tensor(modes, index, x_data, level - 1, dim_ind); } + else if (level == 0) { + double s = 0; + for (auto r = 0; r < components; r++) { + double m = 1.0; + for (dim_t f = 0; f < get_n_modes(); f++) + m *= factors[f](dim_ind[f], r); + s += lambda[r] * m; + } + x_data[index++] = s; } - - Tensor Ktensor::to_tensor() - { - vector modes(get_n_modes()); - for (auto i = 0; i < get_n_modes(); i++) modes[i] = factors[i].get_rows(); - - Tensor X(modes); - int index = 0; - double *x_data = X.get_data(); - auto *dim_ind = new int[get_n_modes()]; - rec_to_tensor(modes, index, x_data, get_n_modes(), dim_ind); - delete[] dim_ind; - return X; - } - - Ktensor &Ktensor::normalize(int mode, int iteration) - { +} + +Tensor Ktensor::to_tensor() { + vector modes(get_n_modes()); + for (dim_t i = 0; i < get_n_modes(); i++) + modes[i] = factors[i].get_rows(); + + Tensor X(modes); + int index = 0; + double *x_data = X.get_data(); + auto *dim_ind = new dim_t[get_n_modes()]; + int levels = static_cast(get_n_modes()); + rec_to_tensor(modes, index, x_data, levels, dim_ind); + delete[] dim_ind; + return X; +} + +Ktensor &Ktensor::normalize(int mode, int iteration) { #pragma omp parallel for // NOLINT(openmp-use-default-none) - for (auto col = 0; col < factors[mode].get_cols(); col++) - { - auto &factor = factors[mode]; - auto *data_ptr = factor.get_data() + col * factor.get_col_stride(); - - if (iteration == 1) - lambda[col] = cblas_dnrm2(factor.get_rows(), data_ptr, 1); - else - { - auto index = cblas_idamax(factor.get_rows(), data_ptr, 1); - assert(index >= 0 && index < factor.get_rows()); - lambda[col] = data_ptr[index]; - } - if (lambda[col] != 0) - cblas_dscal(factor.get_rows(), 1 / lambda[col], data_ptr, 1); + for (dim_t col = 0; col < factors[mode].get_cols(); col++) { + auto &factor = factors[mode]; + auto *data_ptr = factor.get_data() + col * factor.get_col_stride(); + + if (iteration == 1) + lambda[col] = cblas_dnrm2(factor.get_rows(), data_ptr, 1); + else { + auto index = cblas_idamax(factor.get_rows(), data_ptr, 1); + assert(index >= 0 && index < factor.get_rows()); + lambda[col] = data_ptr[index]; } - return *this; + if (lambda[col] != 0) + cblas_dscal(factor.get_rows(), 1 / lambda[col], data_ptr, 1); } - - Ktensor &Ktensor::normalize() - { - for (auto &l : lambda) l = 1.0; - - for (auto n = 0; n < get_n_modes(); n++) - { - auto &factor = factors[n]; - for (auto col = 0; col < get_rank(); col++) - { - auto coeff = cblas_dnrm2(factor.get_rows(), factor.get_data() + col * factor.get_col_stride(), 1); - cblas_dscal(factor.get_rows(), 1 / coeff, factor.get_data() + col * factor.get_col_stride(), 1); - lambda[col] *= coeff; - } + return *this; +} + +Ktensor &Ktensor::normalize() { + for (auto &l : lambda) + l = 1.0; + + for (dim_t n = 0; n < get_n_modes(); n++) { + auto &factor = factors[n]; + for (auto col = 0; col < get_components(); col++) { + auto coeff = cblas_dnrm2(factor.get_rows(), factor.get_data() + col * factor.get_col_stride(), 1); + cblas_dscal(factor.get_rows(), 1 / coeff, factor.get_data() + col * factor.get_col_stride(), 1); + lambda[col] *= coeff; } - normalized = true; - return *this; - } - - Ktensor &Ktensor::denormalize() - { - auto &factor = factors[0]; - for (auto col = 0; col < get_rank(); col++) - cblas_dscal(factor.get_rows(), lambda[col], factor.get_data() + col * factor.get_col_stride(), 1); - normalized = false; - return *this; } - - Ktensor &Ktensor::attach(vector &data_ptrs, bool multi_thread) - { - assert(data_ptrs.size() == get_n_modes()); - - auto th = get_threads(); - if (!multi_thread) - set_threads(1); - - auto index = 0; - for (auto &f : factors) - { - Matrix(f.get_rows(), f.get_cols(), data_ptrs[index]).copy(f); - f.attach(data_ptrs[index++]); - } - - if (!multi_thread) - set_threads(th); - return *this; + normalized = true; + return *this; +} + +Ktensor &Ktensor::denormalize() { + auto &factor = factors[0]; + for (auto col = 0; col < get_components(); col++) + cblas_dscal(factor.get_rows(), lambda[col], factor.get_data() + col * factor.get_col_stride(), 1); + normalized = false; + return *this; +} + +Ktensor &Ktensor::attach(vector &data_ptrs, bool multi_thread) { + assert(data_ptrs.size() == get_n_modes()); + + auto th = get_threads(); + if (!multi_thread) + set_threads(1); + + auto index = 0; + for (auto &f : factors) { + Matrix(f.get_rows(), f.get_cols(), data_ptrs[index]).copy(f); + f.attach(data_ptrs[index++]); } - Ktensor &Ktensor::detach() - { - for (auto &f : factors) - { - auto *old_data = f.get_data(); - f.detach(); - f.copy(Matrix(f.get_rows(), f.get_cols(), old_data)); - Matrix(f.get_rows(), f.get_cols(), old_data).zero(); - } - return *this; + if (!multi_thread) + set_threads(th); + return *this; +} + +Ktensor &Ktensor::detach() { + for (auto &f : factors) { + auto *old_data = f.get_data(); + f.detach(); + f.copy(Matrix(f.get_rows(), f.get_cols(), old_data)); + Matrix(f.get_rows(), f.get_cols(), old_data).zero(); } + return *this; +} - void Ktensor::print(const std::basic_string& text) const - { - using std::cout; - using std::endl; +void Ktensor::print(const std::string &&text) const { + using std::cout; + using std::endl; - cout << "----------------------------------------" << endl; - cout << "Ktensor:"; - cout << "----------------------------------------" << endl; + cout << "----------------------------------------" << endl; + cout << "Ktensor:"; + cout << "----------------------------------------" << endl; - cout << "Rank: " << get_rank() << endl; - cout << "Num Modes: " << get_n_modes() << endl; - cout << "Modes: [ "; - for (const auto &f : get_factors()) cout << f.get_rows() << " "; - cout << "]" << endl; + cout << "Rank: " << get_components() << endl; + cout << "Num Modes: " << get_n_modes() << endl; + cout << "Modes: [ "; + for (const auto &f : get_factors()) + cout << f.get_rows() << " "; + cout << "]" << endl; - cout << "Weights: ["; - for (const auto &l : lambda) cout << l << " "; - cout << " ] " << endl; + cout << "Weights: ["; + for (const auto &l : lambda) + cout << l << " "; + cout << " ] " << endl; - std::string txt = "factor"; - for (auto const &f : get_factors()) f.print(txt); + for (auto const &f : get_factors()) + f.print("factor"); - cout << "----------------------------------------" << endl; - } + cout << "----------------------------------------" << endl; +} #if CUDA_ENABLED - Ktensor &Ktensor::cuattach(vector &cudata_ptrs) - { - assert(cudata_ptrs.size() == get_n_modes()); - - auto index = 0; - for (auto &f : factors) - { - f.cuattach(cudata_ptrs[index++]); - f.send_to_device_async(custream); - } +Ktensor &Ktensor::cuattach(vector &cudata_ptrs, cudaStream_t &stream) { + assert(cudata_ptrs.size() == get_n_modes()); - return *this; + auto index = 0; + for (auto &f : factors) { + f.cuattach(cudata_ptrs[index++]); + f.send_to_device_async(stream); } - Ktensor &Ktensor::cudetach() - { - for (auto &f : factors) - { - cudaMemsetAsync(f.get_cudata(), 0, f.get_n_elements(), custream); - f.cudetach(); - } - return *this; + return *this; +} + +Ktensor &Ktensor::cudetach() { + auto stream = cuda::create_stream(); + for (auto &f : factors) { + cudaMemsetAsync(f.get_cudata(), 0, f.get_n_elements(), stream); + f.cudetach(); } + cudaStreamSynchronize(stream); + return *this; +} #endif } // namespace cals diff --git a/src/matrix.cpp b/src/matrix.cpp index 0176551..a91efc2 100644 --- a/src/matrix.cpp +++ b/src/matrix.cpp @@ -1,118 +1,49 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #include "matrix.h" +#include #include -namespace cals -{ - Matrix::Matrix(const int dim0, const int dim1) - : Tensor{dim0, dim1}, - rows{dim0}, - cols{dim1}, - row_stride{1}, // All matrices stored in col major format by default - col_stride{dim0} - {} - - Matrix::Matrix(int dim0, int dim1, double *view_data) : Matrix(dim0, dim1, 1, dim0, view_data) - {} - - Matrix::Matrix(int dim0, int dim1, int row_stride, int col_stride, double *view_data) - : Tensor{dim0, dim1, view_data}, - rows{dim0}, - cols{dim1}, - row_stride{row_stride}, - col_stride{col_stride} - {} - - Matrix &Matrix::hadamard(const Matrix &mat) - { - assert(!is_view() && !mat.is_view()); - - // TODO improve with element-wise multiplication in MKL - for (auto i = 0; i < get_n_elements(); i++) - get_data()[i] *= mat[i]; - return *this; - } - - void Matrix::print(const std::basic_string& text) const - { - using std::cout; - using std::endl; - - cout << "----------------------------------------" << endl; - cout << text << endl; - cout << "----------------------------------------" << endl; - cout << "Rows: " << rows << ", Cols: " << cols << endl; - cout << "[ " << endl; - for (auto row = 0; row < rows; row++) - { - for (auto col = 0; col < cols; col++) - cout << " " << get_data()[row * row_stride + col * col_stride] << " "; - cout << std::endl; - } - cout << "]" << std::endl; - cout << "----------------------------------------" << endl; +namespace cals { +Matrix::Matrix(dim_t dim0, dim_t dim1) : Tensor{dim0, dim1}, rows{dim0}, cols{dim1}, col_stride{dim0} {} + +Matrix::Matrix(dim_t dim0, dim_t dim1, double *view_data) + : Tensor{dim0, dim1, view_data}, rows{dim0}, cols{dim1}, col_stride{dim0} {} + +Matrix &Matrix::hadamard(const Matrix &mat) { + // TODO improve with element-wise multiplication in MKL + for (dim_t i = 0; i < get_n_elements(); i++) + get_data()[i] *= mat[i]; + return *this; +} + +void Matrix::print(const std::string &&text) const { + using std::cout; + using std::endl; + + cout << "----------------------------------------" << endl; + cout << text << endl; + cout << "----------------------------------------" << endl; + cout << "Rows: " << rows << ", Cols: " << cols << endl; + for (dim_t row = 0; row < rows; row++) { + for (dim_t col = 0; col < cols; col++) + cout << " " << std::setw(6) << (*this)(row, col) << " "; + cout << std::endl; } - - Matrix &Matrix::read(const Matrix &mat) - { - if (this == &mat) // Properly handle self assignment - return *this; - - assert(get_n_elements() == mat.get_n_elements()); - assert(get_rows() == mat.get_rows() && get_cols() == mat.get_cols()); - - if (!is_view() && !mat.is_view()) - cblas_dcopy(get_n_elements(), mat.get_data(), 1, get_data(), 1); - else - for (auto row = 0; row < mat.get_rows(); row++) - for (auto col = 0; col < mat.get_cols(); col++) - get_data()[get_col_stride() * col + get_row_stride() * row] = - mat.get_data()[mat.get_col_stride() * col + mat.get_row_stride() * row]; - return *this; - } - - void Matrix::info() const - { - std::cout << "nRows: " << get_rows() << ", nCols: " << get_cols() << ", nElements: " << get_n_elements() - << ", maxNElements: " << get_max_n_elements() << std::endl; - } - + cout << "----------------------------------------" << endl; +} + +void Matrix::info() const { + std::cout << "nRows: " << get_rows() << ", nCols: " << get_cols() << ", nElements: " << get_n_elements() + << ", maxNElements: " << get_max_n_elements() << std::endl; +} + +Matrix &Matrix::transpose_copy(const Matrix &rhs) { + auto const I = rows; + auto const J = cols; + for (dim_t i = 0; i < I; i++) + for (dim_t j = 0; j < J; j++) + get_data()[j + i * cols] = rhs.get_data()[i + j * rows]; + return *this; +} } // namespace cals diff --git a/src/multi_ktensor.cpp b/src/multi_ktensor.cpp index 09c3247..9af440f 100644 --- a/src/multi_ktensor.cpp +++ b/src/multi_ktensor.cpp @@ -1,53 +1,19 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #include "multi_ktensor.h" -#include "utils.h" +#include + +#include "utils/utils.h" namespace cals { - MultiKtensor::MultiKtensor(vector &modes, int buffer_size) + MultiKtensor::MultiKtensor(vector &modes, dim_t buffer_size) : Ktensor(buffer_size, modes), occupancy(0), - modes(modes), - occupancy_vec(buffer_size) + occupancy_vec(buffer_size), + modes(modes) { - for (auto &f : get_factors()) f.resize(f.get_rows(), 0); + for (auto &f : get_factors()) + f.resize(f.get_rows(), 0); } int MultiKtensor::check_availability(Ktensor &ktensor) @@ -55,10 +21,11 @@ namespace cals auto pos_index = -1; auto rank_counter = 0; auto prev_occ = -1; + auto n_cells = static_cast(occupancy_vec.size()); - for (auto i = 0lu; i < occupancy_vec.size(); i++) + for (auto i = 0; i < n_cells; i++) { - if (rank_counter == ktensor.get_rank()) + if (rank_counter == ktensor.get_components()) break; if (occupancy_vec[i] == 0 && prev_occ != 0) @@ -73,7 +40,7 @@ namespace cals prev_occ = occupancy_vec[i]; } - if (pos_index == -1 || rank_counter != ktensor.get_rank()) + if (pos_index == -1 || rank_counter != ktensor.get_components()) throw BufferFull(); return pos_index; @@ -81,7 +48,16 @@ namespace cals MultiKtensor &MultiKtensor::add(Ktensor &ktensor) { - int pos_index{0}; + if (cuda) + { +#if CUDA_ENABLED + cuda::initialize_stream(stream); +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } + int pos_index; try { pos_index = check_availability(ktensor); @@ -101,7 +77,6 @@ namespace cals // Attach Ktensor ktensor.attach(pos_ptrs); - if (cuda) { #if CUDA_ENABLED @@ -109,36 +84,53 @@ namespace cals auto cuindex = 0; for (auto &f : get_factors()) cupos_ptrs[cuindex++] = f.reset_cudata().get_cudata() + pos_index * f.get_col_stride(); - ktensor.cuattach(cupos_ptrs); + ktensor.cuattach(cupos_ptrs, stream); #else std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); + exit(EXIT_FAILURE); #endif } // Adjust occupancy vector - for (auto i = 0; i < ktensor.get_rank(); i++) occupancy_vec[pos_index + i] = ktensor.get_id(); + for (int i = 0; i < ktensor.get_components(); i++) + occupancy_vec[pos_index + i] = ktensor.get_id(); // adjust occupancy_pct - occupancy += ktensor.get_rank(); + occupancy += ktensor.get_components(); // Create gramians vector gramians(ktensor.get_n_modes()); - for (auto &g : gramians) g = Matrix(ktensor.get_rank(), ktensor.get_rank()); + for (auto &g : gramians) + g = Matrix(ktensor.get_components(), ktensor.get_components()); index = 0; - for (const auto &f : ktensor.get_factors()) ops::update_gramian(f, gramians[index++]); + for (const auto &f : ktensor.get_factors()) + cals::ops::update_gramian(f, gramians[index++]); ktensor.set_iters(1); RegistryEntry entry{ktensor, std::move(gramians), pos_index}; if (line_search) - entry.ls_ktensor = Ktensor(entry.ktensor.get_rank(), modes); + { + entry.ls_ktensor = Ktensor(entry.ktensor.get_components(), modes); + entry.ls_tr_ktensor = Ktensor(entry.ktensor.get_components(), modes); + } // Update registry registry.insert(std::pair(ktensor.get_id(), std::move(entry))); adjust_edges(); + if (cuda) + { +#if CUDA_ENABLED + cudaStreamSynchronize(stream); + cuda::destroy_stream(stream); +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } + return *this; } @@ -156,7 +148,7 @@ namespace cals ktensor.cudetach(); #else std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); + exit(EXIT_FAILURE); #endif } @@ -164,7 +156,7 @@ namespace cals for (auto &el : occupancy_vec) if (el == ktensor_id) el = 0; - occupancy -= ktensor.get_rank(); + occupancy -= ktensor.get_components(); // Update registry registry.erase(ktensor_id); @@ -199,8 +191,17 @@ namespace cals MultiKtensor &MultiKtensor::compress() { - if (1.0 * occupancy / (end - start) < 0.99) - { +// if (1.0 * occupancy / (end - start) < 0.99) +// { + if (cuda) + { +#if CUDA_ENABLED + cuda::initialize_stream(stream); +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } // std::cout << "Occupancy percent: " << 1.0 * occupancy / (end - start) << " Size: " << end << " Compression..."; int col_offset = 0; @@ -230,12 +231,12 @@ namespace cals for (auto &factor : ktensor.get_factors()) new_data.at(index++) = factor.get_data() - offset * factor.get_col_stride(); - if (ktensor.get_rank() < offset) + if (ktensor.get_components() < offset) registry_entry.ktensor.attach(new_data, true); else registry_entry.ktensor.attach(new_data, false); - for (auto i = registry_entry.col; i < registry_entry.col + ktensor.get_rank(); i++) + for (auto i = registry_entry.col; i < registry_entry.col + ktensor.get_components(); i++) std::swap(occupancy_vec[i - offset], occupancy_vec[i]); registry.at(key).col -= offset; @@ -246,7 +247,7 @@ namespace cals { auto *new_cudata = factor.get_cudata() - offset * factor.get_col_stride(); cudaMemcpyAsync(new_cudata, factor.get_cudata(), factor.get_n_elements() * sizeof(double), - cudaMemcpyDeviceToDevice, custream); + cudaMemcpyDeviceToDevice, stream); factor.set_cudata(new_cudata); } #else @@ -261,15 +262,14 @@ namespace cals if (cuda) { #if CUDA_ENABLED - cudaDeviceSynchronize(); + cudaStreamSynchronize(stream); + cuda::destroy_stream(stream); #else std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); + exit(EXIT_FAILURE); #endif } - - } +// } return *this; } } - diff --git a/src/tensor.cpp b/src/tensor.cpp index af1bc3d..103819a 100644 --- a/src/tensor.cpp +++ b/src/tensor.cpp @@ -1,258 +1,189 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #include "tensor.h" #include "ktensor.h" +#include #include #include -#include -#include #include using std::accumulate; -using std::make_unique; -using std::multiplies; using std::cout; using std::endl; +using std::make_unique; +using std::multiplies; -namespace cals -{ - Tensor::Tensor(const vector &modes) - : n_elements{accumulate(modes.cbegin(), modes.cend(), static_cast(1), multiplies<>())}, - max_n_elements{n_elements}, - modes{modes} - { - double *host_ptr = (double *) operator new(n_elements * sizeof(double)); - data_up = unique_ptr(host_ptr, Dopnew()); - data = data_up.get(); +namespace cals { +Tensor::Tensor(const vector &modes) + : n_elements{accumulate(modes.cbegin(), modes.cend(), static_cast(1), multiplies<>())}, + max_n_elements{n_elements}, modes{modes} { + auto *host_ptr = (double *)operator new[](n_elements * sizeof(double), alignment); + data_up = unique_ptr(host_ptr, &Dopnew); + data = data_up.get(); +} + +// Constructor for Matlab, to not copy the tensor twice. +Tensor::Tensor(const vector &modes, double *view_data) + : n_elements{accumulate(modes.cbegin(), modes.cend(), static_cast(1), multiplies<>())}, + max_n_elements{n_elements}, modes{modes}, data_up{nullptr, &Dopnew}, data{view_data} {} + +Tensor::Tensor(const std::string &file_name) { + vector modes_read; + cout << "Reading tensor: " << file_name << endl; + + auto file = std::ifstream(file_name, std::ios::in); + + std::string line; + std::getline(file, line); + std::stringstream ss(line); + + while (ss.good()) { + std::string substr; + getline(ss, substr, ' '); + modes_read.push_back(static_cast(std::strtol(substr.c_str(), nullptr, 10))); } - // Constructor for Matlab, to not copy the tensor twice. - Tensor::Tensor(const vector &modes, double *view_data) - : n_elements{accumulate(modes.cbegin(), modes.cend(), static_cast(1), multiplies<>())}, - max_n_elements{n_elements}, - modes{modes}, - data_up{nullptr}, - data{view_data} - {} - - Tensor::Tensor(const std::string &file_name) - { - vector read_modes; + n_elements = accumulate(modes_read.cbegin(), modes_read.cend(), static_cast(1), multiplies<>()); + max_n_elements = n_elements; + modes = modes_read; - auto file = std::ifstream(file_name, std::ios::in); + auto *host_ptr = (double *)operator new[](n_elements * sizeof(double), alignment); + data_up = unique_ptr(host_ptr, &Dopnew); + data = data_up.get(); - std::string line; - std::getline(file, line); - std::stringstream ss(line); + double val = 0; + dim_t index = 0; + while (file >> val) + data[index++] = val; - while (ss.good()) - { - std::string substr; - getline(ss, substr, ' '); - read_modes.push_back(std::atoi(substr.c_str())); - } + assert(index == n_elements); +} - n_elements = accumulate(read_modes.cbegin(), read_modes.cend(), static_cast(1), multiplies<>()); - max_n_elements = n_elements; - modes = read_modes; - - double *host_ptr = (double *) operator new(n_elements * sizeof(double)); - data_up = unique_ptr(host_ptr, Dopnew()); +Tensor::Tensor(dim_t mode0, dim_t mode1, double *view_data) + : n_elements{mode0 * mode1}, max_n_elements{n_elements}, modes{vector{mode0, mode1}} { + if (view_data == nullptr) { + auto *host_ptr = (double *)operator new[](n_elements * sizeof(double), alignment); + data_up = unique_ptr(host_ptr, &Dopnew); data = data_up.get(); - - double val = 0; - int index = 0; - while (file >> val) - data[index++] = val; - - assert(index == n_elements); - } - - - Tensor::Tensor(const int mode0, const int mode1, double *view_data) - : n_elements{mode0 * mode1}, - max_n_elements{n_elements}, - modes{vector{mode0, mode1}} - { - if (view_data == nullptr) - { - double *host_ptr = (double *) operator new(n_elements * sizeof(double)); - data_up = unique_ptr(host_ptr, Dopnew()); - data = data_up.get(); - } else - { - data_up = nullptr; - data = view_data; - } - } - - Tensor::Tensor(const int rank, const vector &modes) - { - Ktensor P(rank, modes); - P.randomize(); - - *this = P.to_tensor(); - this->rank = rank; - } - - Tensor::Tensor(const Tensor &rhs) - : n_elements{rhs.n_elements}, - modes{rhs.modes} - { - if (!rhs.is_view()) // If copying views, don't allocate new memory and don't copy over data - { - double *host_ptr = (double *) operator new(n_elements * sizeof(double)); - data_up = unique_ptr(host_ptr, Dopnew()); - data = data_up.get(); - cblas_dcopy(n_elements, rhs.get_data(), 1, get_data(), 1); - } else - { - data_up = nullptr; - data = rhs.get_data(); - } - cout << "WARNING: Performed copy constructor =============================" << endl; + } else { + data_up = nullptr; + data = view_data; } +} - Tensor &Tensor::operator=(const Tensor &rhs) - { - if (this == &rhs) // Properly handle self assignment - return *this; - - n_elements = rhs.n_elements; - modes = rhs.modes; +Tensor::Tensor(int rank, const vector &modes) { + Ktensor P(rank, modes); + P.randomize(); - if (!rhs.is_view()) // If copying views, don't allocate new memory and don't copy over data - { - double *host_ptr = (double *) operator new(n_elements * sizeof(double)); - data_up = unique_ptr(host_ptr, Dopnew()); - data = data_up.get(); - cblas_dcopy(n_elements, rhs.get_data(), 1, get_data(), 1); - } else - { - data_up = nullptr; - data = rhs.get_data(); - } + *this = P.to_tensor(); + this->rank = rank; +} - return *this; - } - - Tensor &Tensor::randomize() +Tensor::Tensor(const Tensor &rhs) : n_elements{rhs.n_elements}, modes{rhs.modes} { + if (!rhs.is_view()) // If copying views, don't allocate new memory and don't copy over data { - std::random_device device; -// std::srand(0); - std::mt19937 generator(device()); -// std::mt19937 generator(rand()); - std::uniform_real_distribution dist(-1.0, 1.0); - - fill([&dist, &generator]() -> double - { return dist(generator); }); - return *this; + auto *host_ptr = (double *)operator new[](n_elements * sizeof(double), alignment); + data_up = unique_ptr(host_ptr, &Dopnew); + data = data_up.get(); + cblas_dcopy(n_elements, rhs.get_data(), 1, get_data(), 1); + } else { + data_up = nullptr; + data = rhs.get_data(); } + cout << "WARNING: Performed copy constructor =============================" << endl; +} - Tensor &Tensor::zero() - { - fill([]() -> double - { return static_cast(0.0); }); +Tensor &Tensor::operator=(const Tensor &rhs) { + if (this == &rhs) // Properly handle self assignment return *this; - } - Tensor &Tensor::fill(const std::function &&generator) - { - for (auto i = 0; i < n_elements; i++) data[i] = generator(); - return *this; - } + n_elements = rhs.n_elements; + modes = rhs.modes; - Unfolding Tensor::implicit_unfold(const int mode) const + if (!rhs.is_view()) // If copying views, don't allocate new memory and don't copy over data { - Unfolding unfolding{}; - - auto prod_before = [this](int mode) -> int - { - int prod = 1; - for (auto n = 0; n < mode; n++) - prod *= modes[n]; - return prod; - }; - auto prod_after = [this](int mode) -> int - { - int prod = 1; - for (auto n = mode + 1; n < get_n_modes(); n++) - prod *= modes[n]; - return prod; - }; - - if (mode == 0) - { - unfolding.n_blocks = 1; - unfolding.block_offset = 0; - unfolding.rows = modes[mode]; - unfolding.cols = prod_after(mode); - unfolding.stride = modes[mode]; - } else if (mode == get_n_modes() - 1) - { - unfolding.n_blocks = 1; - unfolding.block_offset = 0; - unfolding.rows = modes[mode]; - unfolding.cols = prod_before(mode); - unfolding.stride = prod_before(mode); - } else //(mode > 0 && mode < n_dims - 1) - { - unfolding.n_blocks = prod_after(mode); - unfolding.block_offset = prod_before(mode) * modes[mode]; // include mode - unfolding.rows = modes[mode]; - unfolding.cols = prod_before(mode); - unfolding.stride = prod_before(mode); - } - return unfolding; + auto *host_ptr = (double *)operator new[](n_elements * sizeof(double), alignment); + data_up = unique_ptr(host_ptr, &Dopnew); + data = data_up.get(); + cblas_dcopy(n_elements, rhs.get_data(), 1, get_data(), 1); + } else { + data_up = nullptr; + data = rhs.get_data(); } - void Tensor::print(const std::basic_string& text) const + return *this; +} + +Tensor &Tensor::randomize() { + std::uniform_real_distribution dist(-1.0, 1.0); + + std::random_device device; + std::mt19937 generator(device()); + fill((function const &&)[&dist, &generator ]()->double { return dist(generator); }); + + return *this; +} + +Tensor &Tensor::zero() { + fill((function const &&)[]()->double { return static_cast(0.0); }); + return *this; +} + +Tensor &Tensor::fill(const std::function &&generator) { + for (dim_t i = 0; i < n_elements; i++) + data[i] = generator(); + return *this; +} + +Unfolding Tensor::implicit_unfold(const dim_t mode) const { + Unfolding unfolding{}; + + auto prod_before = [this](dim_t mode) -> dim_t { + auto prod = static_cast(1); + for (dim_t n = 0; n < mode; n++) + prod *= modes[n]; + return prod; + }; + auto prod_after = [this](dim_t mode) -> dim_t { + auto prod = static_cast(1); + for (dim_t n = mode + 1; n < get_n_modes(); n++) + prod *= modes[n]; + return prod; + }; + + if (mode == 0) { + unfolding.n_blocks = 1; + unfolding.block_offset = 0; + unfolding.rows = modes[mode]; + unfolding.cols = prod_after(mode); + unfolding.stride = modes[mode]; + } else if (mode == get_n_modes() - static_cast(1)) { + unfolding.n_blocks = 1; + unfolding.block_offset = 0; + unfolding.rows = modes[mode]; + unfolding.cols = prod_before(mode); + unfolding.stride = prod_before(mode); + } else //(mode > 0 && mode < n_dims - 1) { - cout << "----------------------------------------" << endl; - cout << "Modes: "; - for (auto const &m : modes) cout << m << " "; - cout << endl; - cout << "data = [ "; - for (auto i = 0; i < n_elements; i++) cout << get_data()[i] << " "; - cout << "]" << endl; - cout << "----------------------------------------" << endl; + unfolding.n_blocks = prod_after(mode); + unfolding.block_offset = prod_before(mode) * modes[mode]; // include mode + unfolding.rows = modes[mode]; + unfolding.cols = prod_before(mode); + unfolding.stride = prod_before(mode); } - + return unfolding; +} + +void Tensor::print(const std::string &&text) const { + cout << "----------------------------------------" << endl; + cout << "Modes: "; + for (auto const &m : modes) + cout << m << " "; + cout << endl; + cout << "data = [ "; + for (dim_t i = 0; i < n_elements; i++) + cout << data[i] << " "; + cout << "]" << endl; + cout << "----------------------------------------" << endl; +} } // namespace cals diff --git a/src/utils.cpp b/src/utils.cpp deleted file mode 100644 index b2cee16..0000000 --- a/src/utils.cpp +++ /dev/null @@ -1,1013 +0,0 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - -#include "utils.h" - -#include -#include -#include -#include -#include - -namespace cals::utils -{ - - std::string mode_string(vector const &modes) - { - std::string m_string; - for (auto const &m : modes) - m_string += std::to_string(m) + '-'; - m_string.pop_back(); - - return m_string; - } - - MttkrpLut read_lookup_table(vector const &modes, int threads) - { - auto lut = MttkrpLut(modes.size()); - - std::string blas_name = CALS_BACKEND; - std::string dir = - "../data/" + blas_name + "/lookup_tables/" + mode_string(modes) + "/" + std::to_string(threads) + "/"; - - for (auto m = 0lu; m < modes.size(); m++) - { - auto file = std::ifstream(dir + std::to_string(m), std::ios::in); - if (file.is_open()) - { - int rank = 0, mttkrp_method = 0; - - while (file >> rank >> mttkrp_method) - lut[m].insert(std::pair(rank, mttkrp_method)); - } else - { - DEBUG(std::cout << "Lookup table missing for tensor: " << mode_string(modes) << ", Threads: " << threads << std::endl;); - return MttkrpLut(); - } - } - return lut; - } -} - -namespace cals::ops -{ - struct Mttkrp2StepGemmParams - { - // Intermediate Matrix - int inter_rows{}; - - // DGEMM parameters - CBLAS_TRANSPOSE trans_A{}; - int n_blocks{}; - int block_offset{}; - int block_rows{}; - int next_block{}; - int stride{}; - int B{}; - }; - - struct Mttkrp2StepGemvParams - { - // DGEMV parameters - CBLAS_TRANSPOSE trans_A{}; - int A_rows{}; - int A_cols{}; - int stride{}; - int x{}; - int y{}; - }; - - void hadamard_all(vector &gramians) - { - for (auto i = 1lu; i < gramians.size(); i++) // skip first gramian - gramians[0].hadamard(gramians[i]); - } - - Matrix &hadamard_but_one(vector &gramians, int mode) - { - // Initialize target gramian - gramians[mode].fill([]() -> double - { return static_cast(1.0); }); - - for (auto i = 0lu; i < gramians.size(); i++) - { - if (i == static_cast(mode)) // ...except mode... - continue; - else // ATA[n] := ATA[n] .* ATA[k] - gramians[mode].hadamard(gramians[i]); - } - return gramians[mode]; - } - - Matrix &khatri_rao(const Matrix &A, const Matrix &B, Matrix &K, KrpParams ¶ms) - { -#if true - { - const int cols = K.get_cols(); - const int rowsA = A.get_rows(); - const int rowsB = B.get_rows(); - -#pragma omp parallel // NOLINT(openmp-use-default-none) - { - int index[2] = {0, 0}; -#pragma omp for // NOLINT(openmp-use-default-none) - for (int col = 0; col < cols; col++) - { - for (int row = 0; row < rowsA * rowsB; row++) - { - // Generate one column of the output. - // Vector hadamard - K.at(row, col) = A.at(index[0], col) * B.at(index[1], col); - - // Bump index. - index[1]++; - if (index[1] >= rowsB) - { - index[1] = 0; -// cuda::send_to_device_async(K.get_data() + col * K.get_col_stride() + index[0] * rowsB, -// K.get_cudata() + col * K.get_col_stride() + index[0] * rowsB, -// rowsB, params.cuda_params.streams[index[0] % cuda_n_streams]); - index[0]++; - } - } - index[0] = 0; - - if (params.cuda) - { -#if CUDA_ENABLED - // cuda::send_to_device_async(K.get_data() + col * K.get_col_stride(), K.get_cudata() + col * K.get_col_stride(), - // K.get_rows(), params.cuda_params.streams[0]); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } - } - }; - - if (params.cuda) - { -#if CUDA_ENABLED - // cudaDeviceSynchronize(); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } - - params.flops += 1llu * K.get_cols() * K.get_rows(); - params.memops += 1llu * K.get_cols() * K.get_rows() + - 1llu * A.get_rows() * A.get_cols() + - 1llu * B.get_rows() * B.get_cols(); - return K; - } - -#else - //#if CUDA_ENABLED - // { - // const int cols = K.get_cols(); - // const int rowsA = A.get_rows(); - // const int rowsB = B.get_rows(); - // - // auto handle = cuda::cublas::create_handle(); - // for (int col = 0; col < cols; col++) - // for (int row = 0; row < rowsA; row++) - // { - // auto *K_pointer = K.get_cudata() + col * K.get_col_stride() + row * rowsB; - // cublasDcopy(handle, rowsB, B.get_cudata() + col * B.get_col_stride(), 1, K_pointer, 1); - // const double val = A.at(row,col); - // cublasDscal(handle, rowsB, &val, K_pointer, 1); - // } - // cuda::cublas::destroy_handle(handle); - // } - // return K; - //#else - // { - // const int cols = K.get_cols(); - // const int rowsA = A.get_rows(); - // const int rowsB = B.get_rows(); - // - //#pragma omp parallel for // NOLINT(openmp-use-default-none) - // for (int col = 0; col < cols; col++) - // for (int row = 0; row < rowsA; row++) - // { - // auto *K_pointer = K.get_data() + col * K.get_col_stride() + row * rowsB; - // cblas_dcopy(rowsB, B.get_data() + col * B.get_col_stride(), 1, K_pointer, 1); - // cblas_dscal(rowsB, A.at(row, col), K_pointer, 1); - // } - // } - //#if CUDA_ENABLED - // K.send_to_device(); - //#endif - // return K; - //#endif -#endif - } - - // Internal recursive implementation of the Khatri-Rao product. - Matrix & - khatri_rao_rec(std::stack &remaining_targets, vector &workspace, int w_index, KrpParams ¶ms) - { - if (!remaining_targets.empty()) - { - auto *factor = remaining_targets.top(); - remaining_targets.pop(); - auto &prev_target = workspace[w_index - 1]; - auto &new_target = workspace[w_index]; - - new_target.resize(prev_target.get_rows() * factor->get_rows(), prev_target.get_cols()); - khatri_rao(prev_target, *factor, new_target, params); - - return khatri_rao_rec(remaining_targets, workspace, ++w_index, params); - } else return workspace[w_index - 1]; - } - - // Compute ⨀ (n != mode) u.factor[n] - Matrix &khatri_rao(Ktensor &u, vector &workspace, int mode, KrpParams ¶ms) - { - params.flops = 0; - params.memops = 0; - - // TODO see if you can preallocate size of stack - std::stack remaining_targets; - for (auto i = 0; i < u.get_n_modes(); i++) - if (i != mode) remaining_targets.push(&(u.get_factor(i))); - - int w_index = 0; - auto *u0 = remaining_targets.top(); - remaining_targets.pop(); - auto *u1 = remaining_targets.top(); - remaining_targets.pop(); - - workspace[w_index].resize(u0->get_rows() * u1->get_rows(), u0->get_cols()); - khatri_rao(*u0, *u1, workspace[w_index], params); - - return khatri_rao_rec(remaining_targets, workspace, ++w_index, params); - } - - Matrix &mttkrp_impl(const Tensor &X, Ktensor &u, vector &workspace, int mode, MttkrpParams ¶ms) - { - // Explicitly generate the Khatri-Rao product - if (params.timer_mttkrp_krp != nullptr) params.timer_mttkrp_krp->start(); - - auto &krp = khatri_rao(u, workspace, mode, params.krp_params); - - if (params.cuda) - { -#if CUDA_ENABLED - krp.send_to_device(); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } - - if (params.timer_mttkrp_krp != nullptr) params.timer_mttkrp_krp->stop(); - - // Compute the matrix product - if (params.timer_mttkrp_gemm != nullptr) params.timer_mttkrp_gemm->start(); - - // Figure out how to implicitly unfold the tensor as one or more matrix blocks. - Unfolding unfolding = X.implicit_unfold(mode); - - if (params.cuda) - { -#if CUDA_ENABLED - auto &handle = params.cuda_params.handle; - auto const &one = params.cuda_params.one; - auto const &zero = params.cuda_params.zero; - - cublasSetStream(handle, params.cuda_params.streams[0]); - if (mode != 0) - { - auto &factor = u.get_factor(mode); - cudaMemsetAsync(factor.get_cudata(), 0, factor.get_n_elements() * sizeof(double), - params.cuda_params.streams[0]); - } - for (auto block_idx = 0; block_idx < unfolding.n_blocks; block_idx++) - { - // Locate block of the mode-th unfolding of X. - double const *X_mode_blk_ptr = X.get_cudata() + block_idx * unfolding.block_offset; - int ldX_mode = unfolding.stride; - - // Locate block of krp. - double const *krp_blk_ptr = krp.get_cudata() + block_idx * unfolding.cols; - - auto &G = u.get_factor(mode); - - if (mode == 0) - cublasDgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, G.get_rows(), G.get_cols(), unfolding.cols, - &one, X_mode_blk_ptr, ldX_mode, krp_blk_ptr, krp.get_col_stride(), - &zero, G.get_cudata(), G.get_col_stride()); - else - cublasDgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N, G.get_rows(), G.get_cols(), unfolding.cols, - &one, X_mode_blk_ptr, ldX_mode, krp_blk_ptr, krp.get_col_stride(), - &one, G.get_cudata(), G.get_col_stride()); - } - u.get_factor(mode).receive_from_device_async(params.cuda_params.streams[0]); -// cudaDeviceSynchronize(); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } else - { - if (mode != 0) u.get_factor(mode).zero(); - for (auto block_idx = 0; block_idx < unfolding.n_blocks; block_idx++) - { - /** - MODE 0 - ==================== - Compute - G := G + X * K - - where - - G and K are column-major - - X is column-major - - No transpositions needed. - - MODES 1, 2, ... - ==================== - Compute - G := G + X * K or rather G := G + (X')' * K - - where - - X is row-major - - G, K are col-major - - We transpose X, since we are using col-major BLAS - **/ - - // Locate block of the mode-th unfolding of X. - double const *X_mode_blk_ptr = X.get_data() + block_idx * unfolding.block_offset; - int ldX_mode = unfolding.stride; - - // Locate block of krp. - double const *krp_blk_ptr = krp.get_data() + block_idx * unfolding.cols; - - auto &G = u.get_factor(mode); - - if (mode == 0) - cblas_dgemm(CblasColMajor, CblasNoTrans, CblasNoTrans, - G.get_rows(), G.get_cols(), unfolding.cols, - 1.0, X_mode_blk_ptr, ldX_mode, krp_blk_ptr, krp.get_col_stride(), - 0.0, G.get_data(), G.get_col_stride()); - else - cblas_dgemm(CblasColMajor, CblasTrans, CblasNoTrans, - G.get_rows(), G.get_cols(), unfolding.cols, - 1.0, X_mode_blk_ptr, ldX_mode, krp_blk_ptr, krp.get_col_stride(), - 1.0, G.get_data(), G.get_col_stride()); - } - } - - if (params.timer_mttkrp_gemm != nullptr) params.timer_mttkrp_gemm->stop(); - - auto &G = u.get_factor(mode); - // GEMM -- blocks * (2 * m * n * k + 2 * m * n) - params.flops = 1llu * unfolding.n_blocks * - (2llu * G.get_rows() * G.get_cols() * unfolding.cols + - 2llu * G.get_rows() * G.get_cols()); - params.memops = 1llu * unfolding.n_blocks * - (1llu * G.get_rows() * unfolding.cols + - 1llu * G.get_cols() * unfolding.cols + - 1llu * G.get_rows() * G.get_cols()); - - params.flops += params.krp_params.flops; - params.memops += params.krp_params.memops; - - return u.get_factor(mode); - } - - Matrix &mttkrp_twostep_impl(const Tensor &X, Ktensor &ktensor, Matrix &workspace, - Mttkrp2StepGemmParams &gemm_params, Mttkrp2StepGemvParams &gemv_params, - MttkrpParams ¶ms) - { - if (params.timer_twostep_gemm != nullptr) params.timer_twostep_gemm->start(); - auto &B = ktensor.get_factor(gemm_params.B); - // Perform TTM - workspace.resize(gemm_params.inter_rows, ktensor.get_rank()); - - if (params.cuda) - { -#if CUDA_ENABLED - auto const &handle = params.cuda_params.handle; - auto const &one = params.cuda_params.one; - auto const &zero = params.cuda_params.zero; - - cublasOperation_t opA; - gemm_params.trans_A == CblasNoTrans ? opA = CUBLAS_OP_N : opA = CUBLAS_OP_T; - - for (auto blk = 0; blk < gemm_params.n_blocks; blk++) - { - cublasSetStream(handle, params.cuda_params.streams[blk % cuda_n_streams]); - cublasDgemm(handle, opA, CUBLAS_OP_N, - gemm_params.block_rows, workspace.get_cols(), B.get_rows(), &one, - X.get_cudata() + blk * gemm_params.block_offset, - gemm_params.stride, // careful with the stride. Is it always this? - B.get_cudata(), B.get_col_stride(), - &zero, workspace.get_cudata() + blk * gemm_params.block_rows, workspace.get_col_stride()); - } -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } else - { -#pragma omp parallel for // NOLINT(openmp-use-default-none) - for (auto blk = 0; blk < gemm_params.n_blocks; blk++) - cblas_dgemm(CblasColMajor, gemm_params.trans_A, CblasNoTrans, - gemm_params.block_rows, workspace.get_cols(), B.get_rows(), 1.0, - X.get_data() + blk * gemm_params.block_offset, - gemm_params.stride, // careful with the stride. Is it always this? - B.get_data(), B.get_col_stride(), - 0.0, workspace.get_data() + blk * gemm_params.block_rows, workspace.get_col_stride()); - } - - if (params.timer_twostep_gemm != nullptr) params.timer_twostep_gemm->stop(); - - - // Perform series of TTVs - if (params.timer_twostep_gemv != nullptr) params.timer_twostep_gemv->start(); - - auto &x = ktensor.get_factor(gemv_params.x); - auto &y = ktensor.get_factor(gemv_params.y); - - if (params.cuda) - { -#if CUDA_ENABLED - for (auto &stream : params.cuda_params.streams) cudaStreamSynchronize(stream); // Make sure all DGEMMS are done - - auto const &handle = params.cuda_params.handle; - auto const &one = params.cuda_params.one; - auto const &zero = params.cuda_params.zero; - - cublasOperation_t opA; - - gemv_params.trans_A == CblasNoTrans ? opA = CUBLAS_OP_N : opA = CUBLAS_OP_T; - for (auto col = 0; col < workspace.get_cols(); col++) - { - cublasSetStream(handle, params.cuda_params.streams[col % cuda_n_streams]); - cublasDgemv(handle, opA, gemv_params.A_rows, gemv_params.A_cols, &one, - workspace.get_cudata() + col * workspace.get_col_stride(), gemv_params.stride, - x.get_cudata() + col * x.get_col_stride(), 1, &zero, - y.get_cudata() + col * y.get_col_stride(), 1); -// y.receive_from_device_async(params.cuda_params.streams[col % cuda_n_streams]); - } - cudaDeviceSynchronize(); - y.receive_from_device(); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } else - { -#pragma omp parallel for // NOLINT(openmp-use-default-none) - for (auto col = 0; col < workspace.get_cols(); col++) - cblas_dgemv(CblasColMajor, gemv_params.trans_A, gemv_params.A_rows, gemv_params.A_cols, 1.0, - workspace.get_data() + col * workspace.get_col_stride(), gemv_params.stride, - x.get_data() + col * x.get_col_stride(), 1, 0.0, - y.get_data() + col * y.get_col_stride(), 1); - } - - if (params.timer_twostep_gemv != nullptr) params.timer_twostep_gemv->stop(); - - - // GEMM -- blocks * (2 * m * n * k + 2 * m * n) - params.flops = 1llu * gemm_params.n_blocks * - (2llu * gemm_params.block_rows * workspace.get_cols() * B.get_rows() + - 2llu * gemm_params.block_rows * workspace.get_cols()); - params.memops = 1llu * gemm_params.n_blocks * - (1llu * gemm_params.block_rows * B.get_rows() + - 1llu * B.get_rows() * workspace.get_cols() + - 1llu * gemm_params.block_rows * workspace.get_cols()); - - // GEMVs -- cols * (2 * m * n) - params.flops += workspace.get_cols() * 2llu * gemv_params.A_rows * gemv_params.A_cols; - params.memops += workspace.get_cols() * (1llu * gemv_params.A_rows * gemv_params.A_cols + 2llu * gemv_params.A_cols); - - return y; - } - - Matrix &mttkrp_twostep(const Tensor &X, Ktensor &u, Matrix &workspace, int mode, MttkrpParams ¶ms) - { - assert(u.get_n_modes() == 3); // Twostep method only implemented for 3D tensors - - auto &U_0 = u.get_factor(0); - auto &U_1 = u.get_factor(1); - auto &U_2 = u.get_factor(2); - - Mttkrp2StepGemmParams gemm_params; - Mttkrp2StepGemvParams gemv_params; - - if (mode == 2) - { - if (params.method == MTTKRP_METHOD::TWOSTEP0) // Option 0 (IK x J) * (J * R) - { - gemm_params.inter_rows = U_1.get_rows() * U_2.get_rows(); - gemm_params.trans_A = CblasTrans; - gemm_params.stride = U_0.get_rows(); - gemm_params.block_rows = gemm_params.inter_rows; - gemm_params.n_blocks = 1; - gemm_params.B = 0; - gemv_params.trans_A = CblasTrans; - gemv_params.A_rows = U_1.get_rows(); - gemv_params.A_cols = U_2.get_rows(); - gemv_params.stride = U_1.get_rows(); - gemv_params.x = 1; - gemv_params.y = 2; - } else // Option 1 (JK x I) * (I * R) - { - gemm_params.inter_rows = U_0.get_rows() * U_2.get_rows(); - gemm_params.trans_A = CblasNoTrans; - gemm_params.block_offset = U_0.get_rows() * U_1.get_rows(); - gemm_params.stride = U_0.get_rows(); - gemm_params.block_rows = U_0.get_rows(); - gemm_params.n_blocks = U_2.get_rows(); - gemm_params.B = 1; - - gemv_params.trans_A = CblasTrans; - gemv_params.A_rows = U_0.get_rows(); - gemv_params.A_cols = U_2.get_rows(); - gemv_params.stride = U_0.get_rows(); - gemv_params.x = 0; - gemv_params.y = 2; - } - } else if (mode == 1) - { - if (params.method == MTTKRP_METHOD::TWOSTEP0) // Option 0 (IJ x K) * (K * R) - { - gemm_params.inter_rows = U_0.get_rows() * U_1.get_rows(); - gemm_params.trans_A = CblasNoTrans; - gemm_params.stride = U_0.get_rows() * U_1.get_rows(); - gemm_params.block_rows = gemm_params.inter_rows; - gemm_params.n_blocks = 1; - gemm_params.B = 2; - gemv_params.trans_A = CblasTrans; - gemv_params.A_rows = U_0.get_rows(); - gemv_params.A_cols = U_1.get_rows(); - gemv_params.stride = U_0.get_rows(); - gemv_params.x = 0; - gemv_params.y = 1; - } else // Option 1 (JK x I) * (I * R) - { - gemm_params.inter_rows = U_1.get_rows() * U_2.get_rows(); - gemm_params.trans_A = CblasTrans; - gemm_params.stride = U_0.get_rows(); - gemm_params.block_rows = gemm_params.inter_rows; - gemm_params.n_blocks = 1; - gemm_params.B = 0; - gemv_params.trans_A = CblasNoTrans; - gemv_params.A_rows = U_1.get_rows(); - gemv_params.A_cols = U_2.get_rows(); - gemv_params.stride = U_1.get_rows(); - gemv_params.x = 2; - gemv_params.y = 1; - } - } else if (mode == 0) - { - if (params.method == MTTKRP_METHOD::TWOSTEP0) // Option 0 (IJ x K) * (K * R) - { - gemm_params.inter_rows = U_0.get_rows() * U_1.get_rows(); - gemm_params.trans_A = CblasNoTrans; - gemm_params.stride = U_0.get_rows() * U_1.get_rows(); - gemm_params.block_rows = gemm_params.inter_rows; - gemm_params.n_blocks = 1; - gemm_params.B = 2; - gemv_params.trans_A = CblasNoTrans; - gemv_params.A_rows = U_0.get_rows(); - gemv_params.A_cols = U_1.get_rows(); - gemv_params.stride = U_0.get_rows(); - gemv_params.x = 1; - gemv_params.y = 0; - } else // Option 1 (IK x J) * (J * R) - { - gemm_params.inter_rows = U_0.get_rows() * U_2.get_rows(); - gemm_params.trans_A = CblasNoTrans; - gemm_params.block_offset = U_0.get_rows() * U_1.get_rows(); - gemm_params.stride = U_0.get_rows(); - gemm_params.block_rows = U_0.get_rows(); - gemm_params.n_blocks = U_2.get_rows(); - gemm_params.B = 1; - - gemv_params.trans_A = CblasNoTrans; - gemv_params.A_rows = U_0.get_rows(); - gemv_params.A_cols = U_2.get_rows(); - gemv_params.stride = U_0.get_rows(); - gemv_params.x = 2; - gemv_params.y = 0; - } - } else - { - std::cerr << "Illegal mode given (" << mode << ") for a " << X.get_n_modes() << "D tensor." << std::endl; - abort(); - } - - return mttkrp_twostep_impl(X, u, workspace, gemm_params, gemv_params, params); - } - - Matrix &mttkrp(const Tensor &X, Ktensor &u, vector &workspace, int mode, MttkrpParams ¶ms) - { - if (X.get_n_modes() != 3) - mttkrp_impl(X, u, workspace, mode, params); - else - { - if (params.method == MTTKRP_METHOD::MTTKRP) - mttkrp_impl(X, u, workspace, mode, params); - else if ((params.method == MTTKRP_METHOD::TWOSTEP0) - || (params.method == MTTKRP_METHOD::TWOSTEP1)) - mttkrp_twostep(X, u, workspace[0], mode, params); // workspace[0] is size maxmode1*maxmode2 x rank - else if (params.method == MTTKRP_METHOD::AUTO) - { - if (params.cuda) // If CUDA, Twostep0 is the best alternative - { - params.method = MTTKRP_METHOD::TWOSTEP0; - mttkrp_twostep(X, u, workspace[0], mode, params); - params.method = MTTKRP_METHOD::AUTO; - } else // No CUDA - { - if (!params.lut.empty() && !params.lut_keys.empty()) // If LUT exists, read it - { - auto key = std::lower_bound(params.lut_keys.cbegin(), params.lut_keys.cend(), u.get_rank()); - if (key == params.lut_keys.end()) - key = params.lut_keys.end() - 1; - auto val = params.lut[mode].at(*key); - if (val == MTTKRP_METHOD::MTTKRP) - mttkrp_impl(X, u, workspace, mode, params); - else if (val == MTTKRP_METHOD::TWOSTEP0 || val == MTTKRP_METHOD::TWOSTEP1) - { - params.method = static_cast(val); - mttkrp_twostep(X, u, workspace[0], mode, params); - params.method = MTTKRP_METHOD::AUTO; - } - } else // If LUT does not exist, revert to common sense defaults - { - if (get_threads() != 1) // If multi-threaded, never Twostep0 - { - if (mode == 1) - mttkrp_impl(X, u, workspace, mode, params); - else - { - params.method = MTTKRP_METHOD::TWOSTEP1; - mttkrp_twostep(X, u, workspace[0], mode, params); // workspace[0] is (maxmode1*maxmode2 x rank) - params.method = MTTKRP_METHOD::AUTO; - } - } else // If single threaded, always Twostep0 - { - params.method = MTTKRP_METHOD::TWOSTEP0; - mttkrp_twostep(X, u, workspace[0], mode, params); // workspace[0] is (maxmode1*maxmode2 x rank) - params.method = MTTKRP_METHOD::AUTO; - } - } - } -// if (std::max({X.get_mode(0), X.get_mode(1), X.get_mode(2)}) > 5 * X.get_mode(mode)) -// mttkrp_twostep(X, u, workspace[0], mode, params); -// else -// mttkrp_impl(X, u, workspace, mode, params); - } else - { - std::cerr << "Invalid MTTKRP method selected." << std::endl; - abort(); - } - } - return u.get_factor(mode); - } - - double compute_fast_error(double X_norm, const vector &lambda, const Matrix &last_factor, - const Matrix &last_mttkrp, const Matrix &gramian_hadamard) - { -// hadamard_all(gramians); - - // Sum the entries of the Hadamard product - double term2 = 0.0; - for (auto j = 0; j < gramian_hadamard.get_cols(); j++) - for (auto i = 0; i < gramian_hadamard.get_rows(); i++) - term2 += lambda[i] * lambda[j] * gramian_hadamard.at(i, j); - - // Compute the inner product of the last factor matrix and the last matrix G. - double term3 = 0.0; - for (auto j = 0; j < last_factor.get_cols(); j++) - for (auto i = 0; i < last_factor.get_rows(); i++) - term3 += lambda[j] * last_factor.at(i, j) * last_mttkrp.at(i, j); - - // Sum up the terms in the approximation error formula. - double fast_error = 0.0; - fast_error = std::fmax(X_norm * X_norm + term2 - 2 * term3, 0); - fast_error = std::sqrt(fast_error); - - return fast_error; - } - - void update_gramian(const Matrix &factor, Matrix &gramian) - { - cblas_dgemm(CblasColMajor, CblasTrans, CblasNoTrans, - gramian.get_rows(), gramian.get_cols(), factor.get_rows(), 1.0, - factor.get_data(), factor.get_col_stride(), - factor.get_data(), factor.get_col_stride(), 0.0, - gramian.get_data(), gramian.get_col_stride()); - } - - double compute_error(const Tensor &X, Ktensor &ktensor, Matrix &krp_workspace, Matrix &ten_workspace) - { - KrpParams krp_params; - const auto &C = ktensor.get_factor(2); - const auto &B = ktensor.get_factor(1); - const auto &A = ktensor.get_factor(0); - - ktensor.denormalize(); - - krp_workspace.resize(B.get_rows() * C.get_rows(), B.get_cols()); - khatri_rao(C, B, krp_workspace, krp_params); - - ten_workspace.resize(A.get_rows(), krp_workspace.get_rows()); - cblas_dgemm(CblasColMajor, CblasNoTrans, CblasTrans, A.get_rows(), krp_workspace.get_rows(), krp_workspace.get_cols(), - 1.0, A.get_data(), A.get_col_stride(), krp_workspace.get_data(), krp_workspace.get_col_stride(), - 0.0, ten_workspace.get_data(), ten_workspace.get_col_stride()); - - for (auto i = 0; i < X.get_n_elements(); i++) ten_workspace[i] = X[i] - ten_workspace[i]; - - ktensor.normalize(); - - return ten_workspace.norm(); - } - - void line_search(Ktensor &ktensor, Ktensor &ls_ktensor, Matrix &krp_workspace, Matrix &ten_workspace, - vector &gramians, const Tensor &X, double X_norm, LineSearchParams ¶ms) - { - for (auto f = 0; f < ls_ktensor.get_n_modes(); f++) - { - const auto ¤t = ktensor.get_factor(f); - auto &old = ls_ktensor.get_factor(f); - - krp_workspace.resize(current.get_rows(), current.get_cols()); - for (auto i = 0; i < old.get_n_elements(); i++) krp_workspace[i] = current[i] - old[i]; - - old.read(current); - cblas_daxpy(old.get_n_elements(), params.step, krp_workspace.get_data(), 1, old.get_data(), 1); - } - for (auto i = 0lu; i < ktensor.get_lambda().size(); i++) ls_ktensor.get_lambda()[i] = ktensor.get_lambda()[i]; - - double error = compute_error(X, ls_ktensor, krp_workspace, ten_workspace); - - const double old_error = ktensor.get_approx_error(); - const double old_fit = ktensor.get_fit(); - - if (error < old_error) - { - DEBUG(std::cout << "Fast forwarding... id: " << ktensor.get_id();); - DEBUG(std::cout << " Old error: " << old_error << std::endl;); - for (auto f = 0; f < ls_ktensor.get_n_modes(); f++) - { - ktensor.get_factor(f).copy(ls_ktensor.get_factor(f)); - - if (params.cuda) - { -#if CUDA_ENABLED - ktensor.get_factor(f).send_to_device_async(custream); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } - - update_gramian(ktensor.get_factor(f), gramians[f]); - } - - ktensor.set_approx_error(error); - ktensor.set_approx_error_diff(std::fabs(error - old_error)); - - const double fit = 1 - std::fabs(error) / X_norm; - ktensor.set_fit(fit); - ktensor.set_fit_diff(std::fabs(fit - old_fit)); - - if (params.cuda) - { -#if CUDA_ENABLED - cudaDeviceSynchronize(); -#else - std::cerr << "Not compiled with CUDA support" << std::endl; - exit(EXIT_FAILURE); -#endif - } - } - } -} - -namespace cals::update -{ - Matrix &calculate_sp(Matrix &ztx, Matrix &ztxp, Matrix &ztz, Matrix &ztzp, vector &R, int &info) - { - auto n_pset = std::count(R.cbegin(), R.cend(), false); - ztxp.resize(n_pset, 1); - ztzp.resize(n_pset, n_pset); - - auto index = 0; - for (auto i = 0lu; i < R.size(); i++) - { - if (R[i] == false) - { - ztxp[index] = ztx[i]; - auto index2 = 0; - for (auto j = 0lu; j < R.size(); j++) - if (R[j] == false) ztzp.at(index, index2++) = ztz.at(i, j); - index++; - } - } - - int oni = 1; - int size = ztzp.get_rows(); - int lda = ztzp.get_col_stride(); - int ldb = ztxp.get_col_stride(); - dposv("L", &size, &oni, ztzp.get_data(), &lda, ztxp.get_data(), &ldb, &info); -// if (info) -// std::cerr << "als_update_factor: DPOSV returned info=" << info << std::endl; - - return ztxp; - } - - Matrix &calculate_lagrangian_multipliers(Matrix &ztx, Matrix &ztz, Matrix &d, Matrix &w) - { - // TODO consider using DSYMV to potentially improve performance for larger (10?) matrices - cblas_dgemv(CblasColMajor, CblasNoTrans, ztz.get_rows(), ztz.get_cols(), - 1.0, ztz.get_data(), ztz.get_rows(), - d.get_data(), d.get_cols(), 0.0, w.get_data(), w.get_cols()); - for (auto i = 0; i < w.get_n_elements(); i++) w[i] = ztx[i] - w[i]; - return w; - } - - // Update the factor matrix by applying non-negativity constraints to the solution. - // factor is assumed to contain the result of the MTTKRP, while gramian contains the hadamard product of all gramians - // (except for the gramian of the target factor matrix) - Matrix &update_factor_non_negative_constrained(Matrix &factor, Matrix &gramian, vector> &active_old) - { - // Create local buffers - const double tol = 10 * 2.2204e-16 * gramian.one_norm() * gramian.get_cols(); - int info = 0; - Matrix ztx(factor.get_cols(), 1); - Matrix ztz(gramian.get_rows(), gramian.get_cols()); - Matrix d(factor.get_cols(), 1); - Matrix w(factor.get_cols(), 1); -// vector active(factor.get_cols(), true); - - Matrix s(factor.get_cols(), 1); - Matrix ztzp(gramian.get_rows(), gramian.get_cols()); - Matrix ztxp(factor.get_cols(), 1); - - // Iterative algorithm applied to each row of the factor matrix - for (int row = 0; row < factor.get_rows(); row++) - { - // Initialize local buffers - d.zero(); - ztz.copy(gramian); - // std::fill(active.begin(), active.end(), true); - for (int i = 0; i < factor.get_cols(); i++) ztx[i] = factor[row + factor.get_rows() * i]; - - // Load previous active set - auto &active = active_old[row]; - - // If all constraints are active, it's the first iteration, so no correction needed. - if (std::find(active.begin(), active.end(), false) != active.end()) - { - info = 0; - auto &sp = calculate_sp(ztx, ztxp, ztz, ztzp, active, info); - if (info) // Cholesky failed, probably due to singularity. Start from d=0. - { - for (int i = 0; i < d.get_n_elements(); i++) active[i] = true; - d.zero(); - } else - { - auto index = 0; - for (auto i = 0lu; i < active.size(); i++) d[i] = active[i] ? 0.0 : sp[index++]; - - // Modified inner loop (if a solution was negative, make it zero, update the active set, and retry) - while (sp.min() < 0) - { - for (int i = 0; i < d.get_n_elements(); i++) if (d[i] < 0.0) d[i] = 0.0; - for (int i = 0; i < d.get_n_elements(); i++) active[i] = (d[i] == 0.0); - - // If at any point we end up with a full active set or a bad cholesky, abort and start from d=0 - if (std::find(active.begin(), active.end(), false) == active.end() || info) - { - for (int i = 0; i < d.get_n_elements(); i++) active[i] = true; - d.zero(); - break; - } - - calculate_sp(ztx, ztxp, ztz, ztzp, active, info); - - if (info) // Cholesky failed, probably due to singularity. Start from d=0. - { - for (int i = 0; i < d.get_n_elements(); i++) active[i] = true; - d.zero(); - break; - } - - index = 0; - for (auto i = 0lu; i < active.size(); i++) d[i] = active[i] ? 0.0 : sp[index++]; - } - } - } - - // Begin Main loop - w = calculate_lagrangian_multipliers(ztx, ztz, d, w); - - while (std::find(active.begin(), active.end(), true) != active.end() && // R ≠ ∅ - w.max(active) > tol) // max(wn) > tol , n in R - { - auto m = w.max_id(active); - active[m] = false; - - auto &sp = calculate_sp(ztx, ztxp, ztz, ztzp, active, info); - - auto index = 0; - for (auto i = 0lu; i < active.size(); i++) s[i] = active[i] ? 0.0 : sp[index++]; - - // Inner Loop - while (sp.min() <= 0) - { - auto a = DBL_MAX; - for (int i = 0; i < d.get_n_elements(); i++) - if (active[i] == false && s[i] <= tol) - { - auto tmp = d[i] / (d[i] - s[i]); - if (tmp < a) a = tmp; - } - - for (int i = 0; i < d.get_n_elements(); i++) - { - d[i] = d[i] + a * (s[i] - d[i]); - assert(d[i] >= 0 || std::fabs(d[i]) < tol); - if (std::fabs(d[i]) < tol && active[i] == false) - { - active[i] = true; - d[i] = 0; - } - } - - calculate_sp(ztx, ztxp, ztz, ztzp, active, info); - - index = 0; - for (auto i = 0lu; i < active.size(); i++) s[i] = active[i] ? 0.0 : sp[index++]; - } - - d.copy(s); - w = calculate_lagrangian_multipliers(ztx, ztz, d, w); - } - for (int i = 0; i < factor.get_cols(); i++) factor[row + factor.get_rows() * i] = d[i]; - } - return factor; - } - - Matrix &update_factor_unconstrained(Matrix &factor, Matrix &gramian) - { - // Solve U_n * H = G for U_n. - int info = 0; - int size = gramian.get_rows(); - int lda = gramian.get_col_stride(); - dpotrf("L", &size, gramian.get_data(), &lda, &info); - if (info) - std::cerr << "als_update_factor: DPORTF returned info=" << info << std::endl; - - cblas_dtrsm(CblasColMajor, CblasRight, CblasLower, CblasTrans, CblasNonUnit, - factor.get_rows(), factor.get_cols(), 1.0, gramian.get_data(), gramian.get_col_stride(), - factor.get_data(), factor.get_col_stride()); - cblas_dtrsm(CblasColMajor, CblasRight, CblasLower, CblasNoTrans, CblasNonUnit, - factor.get_rows(), factor.get_cols(), 1.0, gramian.get_data(), gramian.get_col_stride(), - factor.get_data(), factor.get_col_stride()); - return factor; - } -} diff --git a/src/utils/error.cpp b/src/utils/error.cpp new file mode 100644 index 0000000..83c7e5a --- /dev/null +++ b/src/utils/error.cpp @@ -0,0 +1,95 @@ +#include "error.h" + +#include "utils/mttkrp.h" + +namespace cals::error +{ + + double compute_error(const Tensor &X, Ktensor &ktensor, Matrix &krp_workspace, Matrix &ten_workspace) + { + cals::mttkrp::KrpParams krp_params; + const auto &C = ktensor.get_factor(2); + const auto &B = ktensor.get_factor(1); + const auto &A = ktensor.get_factor(0); + + ktensor.denormalize(); + + krp_workspace.resize(B.get_rows() * C.get_rows(), B.get_cols()); + cals::mttkrp::khatri_rao(C, B, krp_workspace, krp_params); + + ten_workspace.resize(A.get_rows(), krp_workspace.get_rows()); + cblas_dgemm(CblasColMajor, CblasNoTrans, CblasTrans, A.get_rows(), krp_workspace.get_rows(), krp_workspace.get_cols(), + 1.0, A.get_data(), A.get_col_stride(), krp_workspace.get_data(), krp_workspace.get_col_stride(), + 0.0, ten_workspace.get_data(), ten_workspace.get_col_stride()); + +#pragma omp parallel for default(none) shared(X, ten_workspace) + for (dim_t i = 0; i < X.get_n_elements(); i++) + ten_workspace[i] = X[i] - ten_workspace[i]; + + ktensor.normalize(); + + return ten_workspace.norm(); + } + + double compute_error_par(const Tensor &X, Ktensor &ktensor, Ktensor &tr_ktensor) + { + const auto &C = ktensor.get_factor(2); + const auto &B = ktensor.get_factor(1); + const auto &A = ktensor.get_factor(0); + auto &C_tr = tr_ktensor.get_factor(2); + auto &B_tr = tr_ktensor.get_factor(1); + auto &A_tr = tr_ktensor.get_factor(0); + const auto I = A.get_rows(); + const auto J = B.get_rows(); + const auto K = C.get_rows(); + const dim_t L = ktensor.get_components(); + const auto &lambda = ktensor.get_lambda(); + + A_tr.transpose_copy(A); + B_tr.transpose_copy(B); + C_tr.transpose_copy(C); + + double nrm2 = 0; +#pragma omp parallel for shared(lambda, X, A_tr, B_tr, C_tr) reduction(+:nrm2) + for (dim_t k=0; k < K; k++) + for (dim_t j=0; j < J; j++) + for (dim_t i=0; i < I; i++) + { + double tmp = 0.0; + for (dim_t l=0; l < L; l++) + tmp += lambda[l] * A_tr[l + i*L] * B_tr[l + j*L] * C_tr[l + k*L]; + tmp -= X[i + j*I + k*I*J]; + nrm2 += tmp * tmp; + } + + return std::sqrt(nrm2); + } + + double compute_fast_error(double X_norm, + const std::vector &lambda, + const cals::Matrix &last_factor, + const cals::Matrix &last_mttkrp, + const cals::Matrix &gramian_hadamard) + { +// hadamard_all(gramians); + + // Sum the entries of the Hadamard product + double term2 = 0.0; + for (dim_t j = 0; j < gramian_hadamard.get_cols(); j++) + for (dim_t i = 0; i < gramian_hadamard.get_rows(); i++) + term2 += lambda[i] * lambda[j] * gramian_hadamard(i, j); + + // Compute the inner product of the last factor matrix and the last matrix G. + double term3 = 0.0; + for (dim_t j = 0; j < last_factor.get_cols(); j++) + for (dim_t i = 0; i < last_factor.get_rows(); i++) + term3 += lambda[j] * last_factor(i, j) * last_mttkrp(i, j); + + // Sum up the terms in the approximation error formula. + double fast_error = 0.0; + fast_error = std::fmax(X_norm * X_norm + term2 - 2 * term3, 0); + fast_error = std::sqrt(fast_error); + + return fast_error; + } +} diff --git a/src/utils/khatri_rao.cu b/src/utils/khatri_rao.cu new file mode 100644 index 0000000..6c95708 --- /dev/null +++ b/src/utils/khatri_rao.cu @@ -0,0 +1,58 @@ +#ifndef CP_CALS_KHATRI_RAO_CU +#define CP_CALS_KHATRI_RAO_CU + +// #include "stdio.h" + +namespace cals +{ + namespace mttkrp + { + __global__ void khatri_rao_cuda_kernel(double const *const A, + double const *const B, + unsigned int IA, + unsigned int IB, + unsigned int JK, + double *const K) + { + const unsigned int n_threads = blockDim.x * gridDim.x; + const unsigned int IAB = IA * IB; + const unsigned int n_elements = IAB * JK; + + //const unsigned int work_per_thread = ceil((float)n_elements / (float)n_threads); + //const unsigned int thread_idx = blockIdx.x * blockDim.x * work_per_thread + threadIdx.x * work_per_thread; + const unsigned int thread_idx = blockIdx.x * blockDim.x + threadIdx.x; + + //for (auto i = thread_idx; i < thread_idx + work_per_thread; i ++) + for (auto i = thread_idx; i < n_elements; i += n_threads) + { + //if (i < n_elements) + //{ + const unsigned int j = i / IAB; + const unsigned modulo = i % IAB; + const unsigned int iA = modulo / IB; + const unsigned int iB = modulo % IB; + + K[i] = A[iA + j * IA] * B[iB + j * IB]; + //} + } + } + + void khatri_rao_cuda(double *A, + double *B, + unsigned int IA, + unsigned int IB, + unsigned int JK, + double *K) + { + dim3 block_dim; + dim3 grid_dim; + cudaOccupancyMaxPotentialBlockSize(reinterpret_cast(&grid_dim), + reinterpret_cast(&block_dim), + khatri_rao_cuda_kernel); + //printf("Blocks: %d %d %d \n", block_dim.x, block_dim.y, block_dim.z); + //printf("Grids : %d %d %d \n", grid_dim.x, grid_dim.y, grid_dim.z); + khatri_rao_cuda_kernel<<>>(A, B, IA, IB, JK, K); + } + } +} +#endif //CP_CALS_KHATRI_RAO_CU diff --git a/src/utils/line_search.cpp b/src/utils/line_search.cpp new file mode 100644 index 0000000..be127f9 --- /dev/null +++ b/src/utils/line_search.cpp @@ -0,0 +1,134 @@ +#include "utils/line_search.h" + +#include + +#include "utils/error.h" +#include "utils/utils.h" + +using cals::Ktensor; +using cals::Matrix; +using std::vector; + +namespace cals::ls { +void line_search(Ktensor &ktensor, + Ktensor &ls_ktensor, + Matrix &krp_workspace, + Matrix &ten_workspace, + vector &gramians, + const Tensor &X, + double X_norm, + LineSearchParams ¶ms) { + for (dim_t n = 0; n < ls_ktensor.get_n_modes(); n++) { + const auto &curr_factor = ktensor.get_factor(n); + auto &old_factor = ls_ktensor.get_factor(n); + + krp_workspace.resize(curr_factor.get_rows(), curr_factor.get_cols()); + for (dim_t i = 0; i < old_factor.get_n_elements(); i++) + krp_workspace[i] = curr_factor[i] - old_factor[i]; + + old_factor.copy(curr_factor); + cblas_daxpy(old_factor.get_n_elements(), params.step, krp_workspace.get_data(), 1, old_factor.get_data(), 1); + } + for (auto i = 0lu; i < ktensor.get_lambda().size(); i++) + ls_ktensor.get_lambda()[i] = ktensor.get_lambda()[i]; + + double error = cals::error::compute_error(X, ls_ktensor, krp_workspace, ten_workspace); + + const double old_error = ktensor.get_approximation_error(); + + if (error < old_error) { +#if CUDA_ENABLED + auto stream = cuda::create_stream(); +#endif + + DEBUG(std::cout << "Fast forwarding... id: " << ktensor.get_id() << " Old: " << old_error << " New: " << error + << std::endl;); + for (dim_t n = 0; n < ls_ktensor.get_n_modes(); n++) { + ktensor.get_factor(n).copy(ls_ktensor.get_factor(n)); + + if (params.cuda) { +#if CUDA_ENABLED + ktensor.get_factor(n).send_to_device_async(stream); +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } + cals::ops::update_gramian(ktensor.get_factor(n), gramians[n]); + } + + ktensor.set_approximation_error(error); + ktensor.calculate_new_fit(X_norm); + + if (params.cuda) { +#if CUDA_ENABLED + cudaStreamSynchronize(stream); +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } +#if CUDA_ENABLED + cuda::destroy_stream(stream); +#endif + } +} + +void line_search_par(Ktensor &ktensor, + Ktensor &ls_ktensor, + Ktensor &ls_tr_ktensor, + vector &gramians, + const Tensor &X, + double X_norm, + LineSearchParams ¶ms) { +#if CUDA_ENABLED + auto stream = cuda::create_stream(); +#endif + for (dim_t n = 0; n < ls_ktensor.get_n_modes(); n++) { + const auto &curr_factor = ktensor.get_factor(n); + auto &old_factor = ls_ktensor.get_factor(n); + + for (dim_t i = 0; i < old_factor.get_n_elements(); i++) + old_factor[i] = curr_factor[i] + params.step * (curr_factor[i] - old_factor[i]); + } + for (dim_t i = 0; i < ktensor.get_lambda().size(); i++) + ls_ktensor.get_lambda()[i] = ktensor.get_lambda()[i]; + + double error = cals::error::compute_error_par(X, ls_ktensor, ls_tr_ktensor); + + const double old_error = ktensor.get_approximation_error(); + + if (error < old_error) { + DEBUG(std::cout << "Fast forwarding... id: " << ktensor.get_id() << " Old: " << old_error << " New: " << error + << std::endl;); + for (dim_t n = 0; n < ls_ktensor.get_n_modes(); n++) { + ktensor.get_factor(n).copy(ls_ktensor.get_factor(n)); + + if (params.cuda) { +#if CUDA_ENABLED + ktensor.get_factor(n).send_to_device_async(stream); +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } + cals::ops::update_gramian(ktensor.get_factor(n), gramians[n]); + } + + ktensor.set_approximation_error(error); + ktensor.calculate_new_fit(X_norm); + + if (params.cuda) { +#if CUDA_ENABLED + cudaStreamSynchronize(stream); +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } +#if CUDA_ENABLED + cuda::destroy_stream(stream); +#endif + } +} +} // namespace cals::ls diff --git a/src/utils/mttkrp.cpp b/src/utils/mttkrp.cpp new file mode 100644 index 0000000..b7517af --- /dev/null +++ b/src/utils/mttkrp.cpp @@ -0,0 +1,708 @@ +#include "utils/mttkrp.h" + +#include +#include +#include + +#include "utils/utils.h" + +using std::vector; +using std::string; +using cals::Matrix; + +using MTTKRP_TIMERS = cals::MttkrpTimers::TIMERS; + +namespace cals::mttkrp +{ + + void khatri_rao_cuda(double *A, + double *B, + unsigned int IA, + unsigned int IB, + unsigned int JK, + double *K); + + MttkrpLut read_lookup_table(vector const &modes, int threads, bool gpu, bool suppress_warning) + { + auto lut = MttkrpLut(); + lut.lut_v = LUT_v(modes.size()); + + std::string dir{}; + if (gpu) + { + dir = string(SOURCE_DIR) + + "/data/GPU" + + "/lookup_tables/" + + cals::utils::mode_string(modes) + "/"; + } else + { + std::string blas_name = CALS_BACKEND; + dir = string(SOURCE_DIR) + + "/data/" + + blas_name + + "/lookup_tables/" + + cals::utils::mode_string(modes) + + "/" + std::to_string(threads) + "/"; + } + + for (auto m = 0lu; m < modes.size(); m++) + { + auto file = std::ifstream(dir + std::to_string(m), std::ios::in); + if (file.is_open()) + { + int rank = 0, mttkrp_method = 0; + + while (file >> rank >> mttkrp_method) + lut.lut_v[m].insert(std::pair(rank, mttkrp_method)); + } else + { + if (!suppress_warning) + std::cout << "Lookup table missing for tensor: " + << utils::mode_string(modes) + << ", Threads: " << threads + << std::endl; + return MttkrpLut(); + } + } + if (!lut.lut_v.empty()) + { + lut.keys_v.reserve(lut.lut_v[0].size()); + for (auto &[key, val] : lut.lut_v[0]) + lut.keys_v.emplace_back(key); + } + return lut; + } + + struct Mttkrp2StepGemmParams + { + // Intermediate Matrix + dim_t inter_rows{}; + + // DGEMM parameters + CBLAS_TRANSPOSE trans_A{}; + dim_t n_blocks{}; + dim_t block_offset{}; + dim_t block_rows{}; + dim_t next_block{}; + dim_t stride{}; + dim_t B{}; + }; + + struct Mttkrp2StepGemvParams + { + // DGEMV parameters + CBLAS_TRANSPOSE trans_A{}; + dim_t A_rows{}; + dim_t A_cols{}; + dim_t stride{}; + dim_t x{}; + dim_t y{}; + }; + + Matrix &khatri_rao(const Matrix &A, const Matrix &B, Matrix &K, KrpParams ¶ms) + { + +#if true + { + const dim_t cols = K.get_cols(); + const dim_t rowsA = A.get_rows(); + const dim_t rowsB = B.get_rows(); + +#pragma omp parallel for // NOLINT(openmp-use-default-none) + for (dim_t col = 0; col < cols; col++) + { +#pragma omp parallel for // NOLINT(openmp-use-default-none) + for (dim_t rowa = 0; rowa < rowsA; rowa++) + { + auto *const K_pointer = K.get_data() + col * K.get_col_stride() + rowa * rowsB; + auto const factor = A(rowa, col); + +// #pragma clang loop interleave_count(8) // This needs testing. + for (dim_t rowb = 0; rowb < rowsB; rowb++) + K_pointer[rowb] = factor * B[rowb + rowsB * col]; + } + } + + params.flops += 1llu * K.get_cols() * K.get_rows(); + params.memops += 1llu * K.get_cols() * K.get_rows() + + 1llu * A.get_rows() * A.get_cols() + + 1llu * B.get_rows() * B.get_cols(); + return K; + } +#else + //#if CUDA_ENABLED + // { + // const int cols = K.get_cols(); + // const int rowsA = A.get_rows(); + // const int rowsB = B.get_rows(); + // + // auto handle = cuda::cublas::create_handle(); + // for (int col = 0; col < cols; col++) + // for (int row = 0; row < rowsA; row++) + // { + // auto *K_pointer = K.get_cudata() + col * K.get_col_stride() + row * rowsB; + // cublasDcopy(handle, rowsB, B.get_cudata() + col * B.get_col_stride(), 1, K_pointer, 1); + // const double val = A.at(row,col); + // cublasDscal(handle, rowsB, &val, K_pointer, 1); + // } + // cuda::cublas::destroy_handle(handle); + // } + // return K; + //#else + // { + // const int cols = K.get_cols(); + // const int rowsA = A.get_rows(); + // const int rowsB = B.get_rows(); + // + //#pragma omp parallel for // NOLINT(openmp-use-default-none) + // for (int col = 0; col < cols; col++) + // for (int row = 0; row < rowsA; row++) + // { + // auto *K_pointer = K.get_data() + col * K.get_col_stride() + row * rowsB; + // cblas_dcopy(rowsB, B.get_data() + col * B.get_col_stride(), 1, K_pointer, 1); + // cblas_dscal(rowsB, A.at(row, col), K_pointer, 1); + // } + // } + //#if CUDA_ENABLED + // K.send_to_device(); + //#endif + // return K; + //#endif +#endif + }; + + // Internal recursive implementation of the Khatri-Rao product. + Matrix & + khatri_rao_rec(std::stack &remaining_targets, vector &workspace, int w_index, KrpParams ¶ms) + { + if (!remaining_targets.empty()) + { + auto *B = remaining_targets.top(); + remaining_targets.pop(); + auto &A = workspace[w_index - 1]; + auto &K = workspace[w_index]; + + K.resize(A.get_rows() * B->get_rows(), A.get_cols()); + if (params.cuda) + { +#if CUDA_ENABLED + auto &handle = params.cuda_params.handle; + auto &stream = params.cuda_params.streams[0]; + cublasSetStream(handle, stream); + khatri_rao_cuda(A.get_cudata(), B->get_cudata(), + A.get_rows(), B->get_rows(), K.get_cols(), + K.get_cudata()); + cudaStreamSynchronize(stream); // Make sure KRP is done +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } else + khatri_rao(A, *B, K, params); + + params.flops += K.get_n_elements(); + params.memops += 1llu * A.get_n_elements() + B->get_n_elements() + K.get_n_elements(); + + return khatri_rao_rec(remaining_targets, workspace, ++w_index, params); + } else return workspace[w_index - 1]; + } + + // Compute ⨀ (n != mode) u.factor[n] + Matrix &khatri_rao(Ktensor &u, vector &workspace, dim_t mode, KrpParams ¶ms) + { + params.flops = 0; + params.memops = 0; + + // TODO see if you can preallocate size of stack + std::stack remaining_targets; + for (dim_t i = 0; i < u.get_n_modes(); i++) + if (i != mode) + remaining_targets.push(&(u.get_factor(i))); + + int w_index = 0; + auto *u0 = remaining_targets.top(); + remaining_targets.pop(); + auto *u1 = remaining_targets.top(); + remaining_targets.pop(); + + workspace[w_index].resize(u0->get_rows() * u1->get_rows(), u0->get_cols()); + + if (params.cuda) + { +#if CUDA_ENABLED + auto &handle = params.cuda_params.handle; + auto &stream = params.cuda_params.streams[0]; + cublasSetStream(handle, stream); + khatri_rao_cuda((*u0).get_cudata(), (*u1).get_cudata(), + (*u0).get_rows(), (*u1).get_rows(), workspace[w_index].get_cols(), + workspace[w_index].get_cudata()); + cudaStreamSynchronize(stream); // Make sure KRP is done +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } else + khatri_rao(*u0, *u1, workspace[w_index], params); + + params.flops += workspace[w_index].get_n_elements(); + params.memops += 1llu * (*u0).get_n_elements() + (*u1).get_n_elements() + workspace[w_index].get_n_elements(); + + return khatri_rao_rec(remaining_targets, workspace, ++w_index, params); + } + + Matrix &mttkrp_impl(const Tensor &X, + Ktensor &u, + vector &workspace, + int mode, + MttkrpParams ¶ms) + { + // Explicitly generate the Khatri-Rao product + params.mttkrp_timers[MTTKRP_TIMERS::MT_KRP].start(); + + params.krp_params.cuda = params.cuda; + auto &krp = khatri_rao(u, workspace, mode, params.krp_params); + + params.mttkrp_timers[MTTKRP_TIMERS::MT_KRP].stop(); + + // Compute the matrix product + params.mttkrp_timers[MTTKRP_TIMERS::MT_GEMM].start(); + + // Figure out how to implicitly unfold the tensor as one or more matrix blocks. + Unfolding unfolding = X.implicit_unfold(mode); + + if (params.cuda) + { +#if CUDA_ENABLED + auto &handle = params.cuda_params.handle; + auto const &one = params.cuda_params.one; + auto const &zero = params.cuda_params.zero; + auto &stream = params.cuda_params.streams[0]; + + cublasSetStream(handle, stream); + if (mode != 0) + { + auto &factor = u.get_factor(mode); + cudaMemsetAsync(factor.get_cudata(), 0, factor.get_n_elements() * sizeof(double), stream); + } + for (auto block_idx = 0; block_idx < unfolding.n_blocks; block_idx++) + { + // Locate block of the mode-th unfolding of X. + double const *X_mode_blk_ptr = X.get_cudata() + block_idx * unfolding.block_offset; + int ldX_mode = unfolding.stride; + + // Locate block of krp. + double const *krp_blk_ptr = krp.get_cudata() + block_idx * unfolding.cols; + + auto &G = u.get_factor(mode); + + if (mode == 0) + cublasDgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, G.get_rows(), G.get_cols(), unfolding.cols, + &one, X_mode_blk_ptr, ldX_mode, krp_blk_ptr, krp.get_col_stride(), + &zero, G.get_cudata(), G.get_col_stride()); + else + cublasDgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N, G.get_rows(), G.get_cols(), unfolding.cols, + &one, X_mode_blk_ptr, ldX_mode, krp_blk_ptr, krp.get_col_stride(), + &one, G.get_cudata(), G.get_col_stride()); + } + u.get_factor(mode).receive_from_device_async(stream); + cudaStreamSynchronize(stream); // Make sure KRP is done +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } else + { + if (mode != 0) + u.get_factor(mode).zero(); + for (dim_t block_idx = 0; block_idx < unfolding.n_blocks; block_idx++) + { + /** + MODE 0 + ==================== + Compute + G := G + X * K + + where + - G and K are column-major + - X is column-major + + No transpositions needed. + + MODES 1, 2, ... + ==================== + Compute + G := G + X * K or rather G := G + (X')' * K + + where + - X is row-major + - G, K are col-major + + We transpose_copy X, since we are using col-major BLAS + **/ + + // Locate block of the mode-th unfolding of X. + double const *X_mode_blk_ptr = X.get_data() + block_idx * unfolding.block_offset; + dim_t ldX_mode = unfolding.stride; + + // Locate block of krp. + double const *krp_blk_ptr = krp.get_data() + block_idx * unfolding.cols; + + auto &G = u.get_factor(mode); + + if (mode == 0) + cblas_dgemm(CblasColMajor, CblasNoTrans, CblasNoTrans, + G.get_rows(), G.get_cols(), unfolding.cols, + 1.0, X_mode_blk_ptr, ldX_mode, krp_blk_ptr, krp.get_col_stride(), + 0.0, G.get_data(), G.get_col_stride()); + else + cblas_dgemm(CblasColMajor, CblasTrans, CblasNoTrans, + G.get_rows(), G.get_cols(), unfolding.cols, + 1.0, X_mode_blk_ptr, ldX_mode, krp_blk_ptr, krp.get_col_stride(), + 1.0, G.get_data(), G.get_col_stride()); + } + } + + params.mttkrp_timers[MTTKRP_TIMERS::MT_GEMM].stop(); + + auto &G = u.get_factor(mode); + // GEMM -- blocks * (2 * m * n * k + 2 * m * n) + params.flops = 1llu * unfolding.n_blocks * + (2llu * G.get_rows() * G.get_cols() * unfolding.cols + + 2llu * G.get_rows() * G.get_cols()); + params.memops = 1llu * unfolding.n_blocks * + (1llu * G.get_rows() * unfolding.cols + + 1llu * G.get_cols() * unfolding.cols + + 1llu * G.get_rows() * G.get_cols()); + + params.flops += params.krp_params.flops; + params.memops += params.krp_params.memops; + + return u.get_factor(mode); + } + + Matrix &mttkrp_twostep_impl(const Tensor &X, + Ktensor &ktensor, + Matrix &workspace, + Mttkrp2StepGemmParams &gemm_params, + Mttkrp2StepGemvParams &gemv_params, + MttkrpParams ¶ms) + { + params.mttkrp_timers[MTTKRP_TIMERS::TS_GEMM].start(); + auto &B = ktensor.get_factor(gemm_params.B); + // Perform TTM + workspace.resize(gemm_params.inter_rows, ktensor.get_components()); + + if (params.cuda) + { +#if CUDA_ENABLED + auto const &handle = params.cuda_params.handle; + auto const &one = params.cuda_params.one; + auto const &zero = params.cuda_params.zero; + + cublasOperation_t opA = (gemm_params.trans_A == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T; +// gemm_params.trans_A == CblasNoTrans ? opA = CUBLAS_OP_N : opA = CUBLAS_OP_T; + + for (auto blk = 0; blk < gemm_params.n_blocks; blk++) + { + cublasSetStream(handle, params.cuda_params.streams[blk % cuda_n_streams]); + cublasDgemm(handle, opA, CUBLAS_OP_N, + gemm_params.block_rows, workspace.get_cols(), B.get_rows(), &one, + X.get_cudata() + blk * gemm_params.block_offset, + gemm_params.stride, // careful with the stride. Is it always this? + B.get_cudata(), B.get_col_stride(), + &zero, workspace.get_cudata() + blk * gemm_params.block_rows, workspace.get_col_stride()); + } + for (auto &stream : params.cuda_params.streams) + cudaStreamSynchronize(stream); // Make sure all DGEMMS are done +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } else + { +#pragma omp parallel for // NOLINT(openmp-use-default-none) + for (dim_t blk = 0; blk < gemm_params.n_blocks; blk++) + cblas_dgemm(CblasColMajor, gemm_params.trans_A, CblasNoTrans, + gemm_params.block_rows, workspace.get_cols(), B.get_rows(), 1.0, + X.get_data() + blk * gemm_params.block_offset, + gemm_params.stride, // careful with the stride. Is it always this? + B.get_data(), B.get_col_stride(), + 0.0, workspace.get_data() + blk * gemm_params.block_rows, workspace.get_col_stride()); + } + + params.mttkrp_timers[MTTKRP_TIMERS::TS_GEMM].stop(); + + // Perform series of TTVs + params.mttkrp_timers[MTTKRP_TIMERS::TS_GEMV].start(); + + auto &x = ktensor.get_factor(gemv_params.x); + auto &y = ktensor.get_factor(gemv_params.y); + + if (params.cuda) + { +#if CUDA_ENABLED + auto const &handle = params.cuda_params.handle; + auto const &one = params.cuda_params.one; + auto const &zero = params.cuda_params.zero; + + cublasOperation_t opA = (gemv_params.trans_A == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T; + //gemv_params.trans_A == CblasNoTrans ? opA = CUBLAS_OP_N : opA = CUBLAS_OP_T; + + bool flag = false; + double *temp; + if (opA == CUBLAS_OP_N) + { + flag = true; + opA = CUBLAS_OP_T; + std::swap(gemv_params.A_rows, gemv_params.A_cols); + cuda::allocate(temp, workspace.get_n_elements()); + } + + for (auto col = 0; col < workspace.get_cols(); col++) + { + cublasSetStream(handle, params.cuda_params.streams[col % cuda_n_streams]); + if (flag) + { + cublasDgeam(handle, CUBLAS_OP_T, CUBLAS_OP_N, + gemv_params.A_rows, gemv_params.A_cols, &one, + workspace.get_cudata()+ col * workspace.get_col_stride(), gemv_params.A_cols, + &zero, temp + col * workspace.get_col_stride(), gemv_params.A_rows, + temp + col * workspace.get_col_stride(), gemv_params.A_rows); + cublasDgemv(handle, opA, gemv_params.A_rows, gemv_params.A_cols, &one, + temp + col * workspace.get_col_stride(), gemv_params.A_rows, + x.get_cudata() + col * x.get_col_stride(), 1, &zero, + y.get_cudata() + col * y.get_col_stride(), 1); + } + else + cublasDgemv(handle, opA, gemv_params.A_rows, gemv_params.A_cols, &one, + workspace.get_cudata() + col * workspace.get_col_stride(), gemv_params.stride, + x.get_cudata() + col * x.get_col_stride(), 1, &zero, + y.get_cudata() + col * y.get_col_stride(), 1); + } + + for (auto &stream : params.cuda_params.streams) + cudaStreamSynchronize(stream); // Make sure all DGEMVs are done + if (flag) + cuda::deallocate(temp); + y.receive_from_device(); +#else + std::cerr << "Not compiled with CUDA support" << std::endl; + exit(EXIT_FAILURE); +#endif + } else + { +#pragma omp parallel for // NOLINT(openmp-use-default-none) + for (dim_t col = 0; col < workspace.get_cols(); col++) + cblas_dgemv(CblasColMajor, gemv_params.trans_A, gemv_params.A_rows, gemv_params.A_cols, 1.0, + workspace.get_data() + col * workspace.get_col_stride(), gemv_params.stride, + x.get_data() + col * x.get_col_stride(), 1, 0.0, + y.get_data() + col * y.get_col_stride(), 1); + } + + params.mttkrp_timers[MTTKRP_TIMERS::TS_GEMV].stop(); + + // GEMM -- blocks * (2 * m * n * k + 2 * m * n) + params.flops = 1llu * gemm_params.n_blocks * + (2llu * gemm_params.block_rows * workspace.get_cols() * B.get_rows() + + 2llu * gemm_params.block_rows * workspace.get_cols()); + params.memops = 1llu * gemm_params.n_blocks * + (1llu * gemm_params.block_rows * B.get_rows() + + 1llu * B.get_rows() * workspace.get_cols() + + 1llu * gemm_params.block_rows * workspace.get_cols()); + + // GEMVs -- cols * (2 * m * n) + params.flops += workspace.get_cols() * 2llu * gemv_params.A_rows * gemv_params.A_cols; + params.memops += + workspace.get_cols() * (1llu * gemv_params.A_rows * gemv_params.A_cols + 2llu * gemv_params.A_cols); + + return y; + } + + Matrix &mttkrp_twostep(const Tensor &X, + Ktensor &u, + Matrix &workspace, + int mode, + MttkrpParams ¶ms) + { + assert(u.get_n_modes() == 3); // Twostep method only implemented for 3D tensors + + auto &U_0 = u.get_factor(0); + auto &U_1 = u.get_factor(1); + auto &U_2 = u.get_factor(2); + + Mttkrp2StepGemmParams gemm_params; + Mttkrp2StepGemvParams gemv_params; + + if (mode == 2) + { + if (params.method == MTTKRP_METHOD::TWOSTEP0) // Option 0 (JK x I) * (I * R) + { + gemm_params.inter_rows = U_1.get_rows() * U_2.get_rows(); + gemm_params.trans_A = CblasTrans; + gemm_params.stride = U_0.get_rows(); + gemm_params.block_rows = gemm_params.inter_rows; + gemm_params.n_blocks = 1; + gemm_params.B = 0; + gemv_params.trans_A = CblasTrans; + gemv_params.A_rows = U_1.get_rows(); + gemv_params.A_cols = U_2.get_rows(); + gemv_params.stride = U_1.get_rows(); + gemv_params.x = 1; + gemv_params.y = 2; + } else // Option 1 (IK x J) * (J * R) + { + gemm_params.inter_rows = U_0.get_rows() * U_2.get_rows(); + gemm_params.trans_A = CblasNoTrans; + gemm_params.block_offset = U_0.get_rows() * U_1.get_rows(); + gemm_params.stride = U_0.get_rows(); + gemm_params.block_rows = U_0.get_rows(); + gemm_params.n_blocks = U_2.get_rows(); + gemm_params.B = 1; + + gemv_params.trans_A = CblasTrans; + gemv_params.A_rows = U_0.get_rows(); + gemv_params.A_cols = U_2.get_rows(); + gemv_params.stride = U_0.get_rows(); + gemv_params.x = 0; + gemv_params.y = 2; + } + } else if (mode == 1) + { + if (params.method == MTTKRP_METHOD::TWOSTEP0) // Option 0 (IJ x K) * (K * R) + { + gemm_params.inter_rows = U_0.get_rows() * U_1.get_rows(); + gemm_params.trans_A = CblasNoTrans; + gemm_params.stride = U_0.get_rows() * U_1.get_rows(); + gemm_params.block_rows = gemm_params.inter_rows; + gemm_params.n_blocks = 1; + gemm_params.B = 2; + gemv_params.trans_A = CblasTrans; + gemv_params.A_rows = U_0.get_rows(); + gemv_params.A_cols = U_1.get_rows(); + gemv_params.stride = U_0.get_rows(); + gemv_params.x = 0; + gemv_params.y = 1; + } else // Option 1 (JK x I) * (I * R) + { + gemm_params.inter_rows = U_1.get_rows() * U_2.get_rows(); + gemm_params.trans_A = CblasTrans; + gemm_params.stride = U_0.get_rows(); + gemm_params.block_rows = gemm_params.inter_rows; + gemm_params.n_blocks = 1; + gemm_params.B = 0; + gemv_params.trans_A = CblasNoTrans; + gemv_params.A_rows = U_1.get_rows(); + gemv_params.A_cols = U_2.get_rows(); + gemv_params.stride = U_1.get_rows(); + gemv_params.x = 2; + gemv_params.y = 1; + } + } else if (mode == 0) + { + if (params.method == MTTKRP_METHOD::TWOSTEP0) // Option 0 (IJ x K) * (K * R) + { + gemm_params.inter_rows = U_0.get_rows() * U_1.get_rows(); + gemm_params.trans_A = CblasNoTrans; + gemm_params.stride = U_0.get_rows() * U_1.get_rows(); + gemm_params.block_rows = gemm_params.inter_rows; + gemm_params.n_blocks = 1; + gemm_params.B = 2; + gemv_params.trans_A = CblasNoTrans; + gemv_params.A_rows = U_0.get_rows(); + gemv_params.A_cols = U_1.get_rows(); + gemv_params.stride = U_0.get_rows(); + gemv_params.x = 1; + gemv_params.y = 0; + } else // Option 1 (IK x J) * (J * R) + { + gemm_params.inter_rows = U_0.get_rows() * U_2.get_rows(); + gemm_params.trans_A = CblasNoTrans; + gemm_params.block_offset = U_0.get_rows() * U_1.get_rows(); + gemm_params.stride = U_0.get_rows(); + gemm_params.block_rows = U_0.get_rows(); + gemm_params.n_blocks = U_2.get_rows(); + gemm_params.B = 1; + + gemv_params.trans_A = CblasNoTrans; + gemv_params.A_rows = U_0.get_rows(); + gemv_params.A_cols = U_2.get_rows(); + gemv_params.stride = U_0.get_rows(); + gemv_params.x = 2; + gemv_params.y = 0; + } + } else + { + std::cerr << "Illegal mode given (" << mode << ") for a " << X.get_n_modes() << "D tensor." << std::endl; + abort(); + } + + return mttkrp_twostep_impl(X, u, workspace, gemm_params, gemv_params, params); + } + + Matrix &mttkrp(const Tensor &X, + Ktensor &u, + vector &workspace, + int mode, + MttkrpParams ¶ms) + { + // Make sure all timers are zero, bc some of the timers are not going to be set later, depending on chosen branch. + for (auto i = 0; i < params.mttkrp_timers.LENGTH; i++) + params.mttkrp_timers.timers[i].reset(); + + if (X.get_n_modes() != 3) + mttkrp_impl(X, u, workspace, mode, params); + else + { + if (params.method == MTTKRP_METHOD::MTTKRP) + mttkrp_impl(X, u, workspace, mode, params); + else if ((params.method == MTTKRP_METHOD::TWOSTEP0) + || (params.method == MTTKRP_METHOD::TWOSTEP1)) + mttkrp_twostep(X, u, workspace[0], mode, params); // workspace[0] is size maxmode1*maxmode2 x rank + else if (params.method == MTTKRP_METHOD::AUTO) + { + if (!params.lut.lut_v.empty() && !params.lut.keys_v.empty()) // If LUT exists, read it + { + auto key = std::lower_bound(params.lut.keys_v.cbegin(), params.lut.keys_v.cend(), u.get_components()); + if (key == params.lut.keys_v.end()) + key = params.lut.keys_v.end() - 1; + auto val = params.lut.lut_v[mode].at(*key); + if (val == MTTKRP_METHOD::MTTKRP) + mttkrp_impl(X, u, workspace, mode, params); + else if (val == MTTKRP_METHOD::TWOSTEP0 || val == MTTKRP_METHOD::TWOSTEP1) + { + params.method = static_cast(val); + mttkrp_twostep(X, u, workspace[0], mode, params); + params.method = MTTKRP_METHOD::AUTO; + } + } else // If LUT does not exist, revert to common sense defaults + { + if (params.cuda) // If CUDA, MTTKRP is the best alternative + mttkrp_impl(X, u, workspace, mode, params); + else + if (get_threads() != 1) // If multi-threaded, never Twostep0 + { + if (mode == 1) + mttkrp_impl(X, u, workspace, mode, params); + else + { + params.method = MTTKRP_METHOD::TWOSTEP1; + mttkrp_twostep(X, u, workspace[0], mode, params); // workspace[0] is (maxmode1*maxmode2 x rank) + params.method = MTTKRP_METHOD::AUTO; + } + } else // If single threaded, always Twostep0 + { + params.method = MTTKRP_METHOD::TWOSTEP0; + mttkrp_twostep(X, u, workspace[0], mode, params); // workspace[0] is (maxmode1*maxmode2 x rank) + params.method = MTTKRP_METHOD::AUTO; + } + } + } else + { + std::cerr << "Invalid MTTKRP method selected." << std::endl; + abort(); + } + } + return u.get_factor(mode); + } + +} diff --git a/src/utils/update.cpp b/src/utils/update.cpp new file mode 100644 index 0000000..3c2cd87 --- /dev/null +++ b/src/utils/update.cpp @@ -0,0 +1,194 @@ +#include "utils/update.h" + +#include +#include + +using cals::Matrix; +using std::vector; + +namespace cals::update { +struct ZeroPassiveSet : public std::exception { + const char *what() const noexcept override { return "Passive set is zero."; } +}; + +struct CholFail : public std::exception { + const char *what() const noexcept override { return "Cholesky failed due to Matrix not beind SPD."; } +}; + +Matrix &calculate_sp(const Matrix &y, Matrix &sp, const Matrix &G, Matrix &Gp, const vector &active) { + int n_active = static_cast(std::count(active.cbegin(), active.cend(), false)); + sp.resize(n_active, 1); + Gp.resize(n_active, n_active); + + auto index = 0; + for (int i = 0; i < static_cast(active.size()); i++) { + if (!active[i]) { + sp[index] = y[i]; + auto index2 = 0; + for (int j = 0; j < static_cast(active.size()); j++) + if (!active[j]) // TODO: Invert the index and index2++ for contiguous traversal + Gp(index, index2++) = G(i, j); + index++; + } + } + + int info = 0; + int rhs = 1; + int n = n_active; + int lda = n_active; + int ldb = n_active; + dposv("L", &n, &rhs, Gp.get_data(), &lda, sp.get_data(), &ldb, &info); + if (info < 0) + std::cerr << "Parameter invalid. DPOSV returned info=" << info << std::endl; + else if (info > 0) + throw CholFail(); + + return sp; +} + +Matrix &calculate_lagrangian_multipliers(const Matrix &y, const Matrix &G, const Matrix &d, Matrix &w) { + // TODO consider using DSYMV to potentially improve performance for larger (10?) matrices + cblas_dgemv(CblasColMajor, CblasNoTrans, G.get_rows(), G.get_cols(), 1.0, G.get_data(), G.get_rows(), d.get_data(), + d.get_cols(), 0.0, w.get_data(), w.get_cols()); + for (dim_t i = 0; i < w.get_n_elements(); i++) + w[i] = y[i] - w[i]; + return w; +} + +// Update the factor matrix by applying non-negativity constraints to the solution. +// factor is assumed to contain the result of the MTTKRP, while gramian contains the hadamard product of all gramians +// (except for the gramian of the target factor matrix) +Matrix &update_factor_non_negative_constrained(Matrix &factor, Matrix &gramian, vector> &active_old) { + Matrix &G = gramian; + int n = factor.get_cols(); + + const double eps = 2.2204e-16; + const double tol = 10 * eps * gramian.one_norm() * n; + + // Create local buffers + Matrix y(n, 1); + Matrix d(n, 1); + Matrix w(n, 1); +// vector active(static_cast(n), true); + + Matrix s(n, 1); + Matrix sp(n, 1); + Matrix Gp(n, n); + + // Iterative algorithm applied to each row of the factor matrix + for (dim_t row = 0; row < factor.get_rows(); row++) { + // Initialize local buffers + d.zero(); + + auto &active = active_old[row]; + // Get row and determine previous active set + for (int i = 0; i < n; i++) { + y[i] = factor[row + factor.get_rows() * i]; + if (y[i] > 0) + active[i] = false; + } + + // If all constraints are active, it's the first iteration, so no correction needed. + if (std::find(active.begin(), active.end(), false) != active.end()) { + try { + calculate_sp(y, sp, G, Gp, active); + + auto index = 0; + for (auto i = 0; i < n; i++) + d[i] = active[i] ? 0.0 : sp[index++]; + + // Modified inner loop (if a solution was negative, make it zero, update the active set, and retry) + while (sp.min() <= tol) { + for (int i = 0; i < n; i++) + if (d[i] <= tol) { + d[i] = 0.0; + active[i] = (d[i] == 0.0); + } + + if (std::find(active.begin(), active.end(), false) == active.end()) + throw ZeroPassiveSet(); + + sp = calculate_sp(y, sp, G, Gp, active); + + index = 0; + for (auto i = 0lu; i < active.size(); i++) + d[i] = active[i] ? 0.0 : sp[index++]; + } + } catch (std::exception &exception) { + std::fill(active.begin(), active.end(), true); + d.zero(); + } + } + + w = calculate_lagrangian_multipliers(y, G, d, w); + + // Main loop + while (std::find(active.begin(), active.end(), true) != active.end() && // R ≠ ∅ + w.max(active) > tol) // max(wn) > tol , n in R + { + auto m = w.max_id(active); + active[m] = false; + + calculate_sp(y, sp, G, Gp, active); + + // Inner Loop + while (sp.min() <= tol) { + auto index = 0; + for (auto i = 0lu; i < active.size(); i++) + s[i] = active[i] ? 0.0 : sp[index++]; + + auto a = std::numeric_limits::max(); + for (int i = 0; i < n; i++) + if (!active[i] && s[i] <= tol) { + auto tmp = d[i] / (d[i] - s[i]); + if (tmp < a) + a = tmp; + } + if (a >= 1e20) + std::cerr << "Bad a in NNLS" << std::endl; + + for (dim_t i = 0; i < d.get_n_elements(); i++) { + d[i] = d[i] + a * (s[i] - d[i]); + assert(d[i] >= 0 || std::fabs(d[i]) < tol); + if (std::fabs(d[i]) < tol && !active[i]) { + active[i] = true; + d[i] = 0; + } + } + sp = calculate_sp(y, sp, G, Gp, active); + } + + if (sp.min() < 0) + std::cerr << "CRITICAL ____________________________________" << std::endl; + + auto index = 0; + for (auto i = 0lu; i < active.size(); i++) + d[i] = active[i] ? 0.0 : sp[index++]; + + w = calculate_lagrangian_multipliers(y, G, d, w); + } + + for (int i = 0; i < n; i++) + factor[row + factor.get_rows() * i] = d[i]; + } + + return factor; +} + +Matrix &update_factor_unconstrained(Matrix &factor, Matrix &gramian) { + // Solve U_n * H = G for U_n. + int info = 0; + int size = gramian.get_rows(); + int lda = gramian.get_col_stride(); + dpotrf("L", &size, gramian.get_data(), &lda, &info); + if (info) + std::cerr << "als_update_factor: DPORTF returned info=" << info << std::endl; + + cblas_dtrsm(CblasColMajor, CblasRight, CblasLower, CblasTrans, CblasNonUnit, factor.get_rows(), factor.get_cols(), + 1.0, gramian.get_data(), gramian.get_col_stride(), factor.get_data(), factor.get_col_stride()); + cblas_dtrsm(CblasColMajor, CblasRight, CblasLower, CblasNoTrans, CblasNonUnit, factor.get_rows(), factor.get_cols(), + 1.0, gramian.get_data(), gramian.get_col_stride(), factor.get_data(), factor.get_col_stride()); + return factor; +} + +} // namespace cals::update diff --git a/src/utils/utils.cpp b/src/utils/utils.cpp new file mode 100644 index 0000000..d7e9e81 --- /dev/null +++ b/src/utils/utils.cpp @@ -0,0 +1,38 @@ +#include "utils/utils.h" + +namespace cals::utils { +std::string mode_string(vector const &modes) { + std::string m_string; + for (auto const &m : modes) + m_string += std::to_string(m) + '-'; + m_string.pop_back(); + + return m_string; +} +} // namespace cals::utils + +namespace cals::ops { +void hadamard_all(vector &matrices) { + for (auto i = 1lu; i < matrices.size(); i++) // skip first gramian + matrices[0].hadamard(matrices[i]); +} + +Matrix &hadamard_but_one(vector &matrices, int mode) { + // Initialize target matrix + matrices[mode].fill((function const &&)[]()->double { return static_cast(1.0); }); + + for (auto i = 0lu; i < matrices.size(); i++) { + if (i == static_cast(mode)) // ...except mode... + continue; + else // ATA[n] := ATA[n] .* ATA[k] + matrices[mode].hadamard(matrices[i]); + } + return matrices[mode]; +} + +void update_gramian(const Matrix &factor, Matrix &gramian) { + cblas_dgemm(CblasColMajor, CblasTrans, CblasNoTrans, gramian.get_rows(), gramian.get_cols(), factor.get_rows(), 1.0, + factor.get_data(), factor.get_col_stride(), factor.get_data(), factor.get_col_stride(), 0.0, + gramian.get_data(), gramian.get_col_stride()); +} +} // namespace cals::ops diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 546c771..da494d2 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -1,65 +1,18 @@ -#@HEADER -# ****************************************************************************** -# -# CP-CALS: Software for computing the Canonical Polyadic Decomposition using -# the Concurrent Alternating Least Squares Algorithm. -# -# Copyright (c) 2020, Christos Psarras -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -# ****************************************************************************** -#@HEADER - -SET(tests als cals) - function(create_test test source) - SET(BLA_VEN MKL) - if (${WITH_BLIS}) - SET(BLA_VEN BLIS) - endif () - if (${WITH_OPENBLAS}) - SET(BLA_VEN OPENBLAS) - endif () - - SET(name ${test}_${BLA_VEN}) - ADD_EXECUTABLE(${name} ${source}.cpp) + set(name ${test}_${BLA_VEN}) + add_executable(${name} ${source}.cpp) if (GTEST_DEPENDENCY) - ADD_DEPENDENCIES(${name} ${GTEST_DEPENDENCY}) + add_dependencies(${name} ${GTEST_DEPENDENCY}) endif () - TARGET_LINK_LIBRARIES(${name} PRIVATE CALS_${BLA_VEN}) - TARGET_LINK_LIBRARIES(${name} PRIVATE ${GTEST_LINK_FLAGS}) - - ADD_TEST(NAME ${name} COMMAND ${name}) + target_link_libraries(${name} PRIVATE CALS_${BLA_VEN}) + target_link_libraries(${name} PRIVATE ${GTEST_LINK_FLAGS}) + add_test(NAME ${name} COMMAND ${name}) endfunction(create_test) +set(tests als cals) foreach (test_dir ${tests}) - ADD_SUBDIRECTORY(${test_dir}) + add_subdirectory(${test_dir}) endforeach () diff --git a/tests/als/CMakeLists.txt b/tests/als/CMakeLists.txt index b0e5733..534b0f3 100644 --- a/tests/als/CMakeLists.txt +++ b/tests/als/CMakeLists.txt @@ -1,42 +1,4 @@ -#@HEADER -# ****************************************************************************** -# -# CP-CALS: Software for computing the Canonical Polyadic Decomposition using -# the Concurrent Alternating Least Squares Algorithm. -# -# Copyright (c) 2020, Christos Psarras -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -# ****************************************************************************** -#@HEADER - set(tests test_als) - foreach (test ${tests}) create_test(test.als.${test} ${test}) endforeach () \ No newline at end of file diff --git a/tests/als/test_als.cpp b/tests/als/test_als.cpp index 31992f1..99a8db0 100644 --- a/tests/als/test_als.cpp +++ b/tests/als/test_als.cpp @@ -1,178 +1,150 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #include "gtest/gtest.h" #include #include -#include "tensor.h" #include "als.h" #define MODEL_DIFF_ACC 1e-09 -TEST(Als, ComputeCorrectResult3D) -{ - vector modes = {9, 4, 2}; +TEST(Als, ComputeCorrectResult3D) { + vector modes = {9, 4, 2}; cals::Tensor X(5, modes); cals::Ktensor k(5, modes); - cals::ops::MTTKRP_METHOD mttkrp_variants[] = { - cals::ops::MTTKRP_METHOD::MTTKRP, - cals::ops::MTTKRP_METHOD::TWOSTEP0, - cals::ops::MTTKRP_METHOD::TWOSTEP1, - cals::ops::MTTKRP_METHOD::AUTO - }; + cals::mttkrp::MTTKRP_METHOD mttkrp_variants[] = { + cals::mttkrp::MTTKRP_METHOD::MTTKRP, cals::mttkrp::MTTKRP_METHOD::TWOSTEP0, cals::mttkrp::MTTKRP_METHOD::TWOSTEP1, + cals::mttkrp::MTTKRP_METHOD::AUTO}; - for (auto p = 0; p < 20; p++) - { + for (auto p = 0; p < 20; p++) { double errors[4] = {0.0}; auto index = 0; k.randomize(); - for (auto &method : mttkrp_variants) - { + for (auto &method : mttkrp_variants) { cals::AlsParams params; params.max_iterations = 100; params.mttkrp_method = method; - params.line_search = true; + params.line_search = false; params.line_search_interval = 5; params.line_search_step = 0.9; + params.suppress_lut_warning = true; +#if CUDA_ENABLED + std::random_device rd; + std::default_random_engine eng(rd()); + std::uniform_int_distribution distr(0, 1); + if (distr(eng) == 0) + params.cuda = true; + else + params.cuda = false; +#else + params.cuda = false; +#endif cals::Ktensor k_copy(k); auto report = cp_als(X, k_copy, params); // Get the slow error calculated by reconstructing the tensor, elementwise subtracting and norming - auto result_tensor = k_copy.to_tensor(); - for (auto i = 0; i < X.get_n_elements(); i++) result_tensor[i] = X[i] - result_tensor[i]; - auto slow_error = result_tensor.norm(); + auto approx_X = k_copy.to_tensor(); + for (dim_t i = 0; i < X.get_n_elements(); i++) + approx_X[i] = X[i] - approx_X[i]; + auto slow_error = approx_X.norm(); errors[index++] = slow_error; EXPECT_FALSE(std::isnan(slow_error)); EXPECT_LT(slow_error, 50); } - for (auto e : errors) EXPECT_NEAR(e, errors[0], 1e-8); + for (auto e : errors) + EXPECT_NEAR(e, errors[0], 1e-8); } } -TEST(Als, ComputeCorrectResultConstrained3D) -{ - vector modes = {9, 4, 2}; +TEST(Als, ComputeCorrectResultConstrained3D) { + vector modes = {18, 17, 16}; cals::Tensor X(5, modes); cals::Ktensor k(5, modes); - cals::ops::MTTKRP_METHOD mttkrp_variants[] = { - cals::ops::MTTKRP_METHOD::MTTKRP, - cals::ops::MTTKRP_METHOD::TWOSTEP0, - cals::ops::MTTKRP_METHOD::TWOSTEP1, - cals::ops::MTTKRP_METHOD::AUTO - }; + cals::mttkrp::MTTKRP_METHOD mttkrp_variants[] = { + cals::mttkrp::MTTKRP_METHOD::MTTKRP, cals::mttkrp::MTTKRP_METHOD::TWOSTEP0, cals::mttkrp::MTTKRP_METHOD::TWOSTEP1, + cals::mttkrp::MTTKRP_METHOD::AUTO}; - for (auto p = 0; p < 20; p++) - { + for (auto p = 0; p < 20; p++) { double errors[4] = {0.0}; auto index = 0; k.randomize(); - for (auto &method : mttkrp_variants) - { + for (auto &method : mttkrp_variants) { cals::AlsParams params; params.max_iterations = 100; params.mttkrp_method = method; params.update_method = cals::update::UPDATE_METHOD::NNLS; + params.suppress_lut_warning = true; cals::Ktensor k_copy(k); auto report = cp_als(X, k_copy, params); + for (auto &f : k_copy.get_factors()) + for (auto i = 0; i < f.get_n_elements(); i++) + EXPECT_GE(f[i], 0.0); + // Get the slow error calculated by reconstructing the tensor, elementwise subtracting and norming - auto result_tensor = k_copy.to_tensor(); - for (auto i = 0; i < X.get_n_elements(); i++) result_tensor[i] = X[i] - result_tensor[i]; - auto slow_error = result_tensor.norm(); + auto approx_X = k_copy.to_tensor(); + for (dim_t i = 0; i < X.get_n_elements(); i++) + approx_X[i] = X[i] - approx_X[i]; + auto slow_error = approx_X.norm(); errors[index++] = slow_error; EXPECT_FALSE(std::isnan(slow_error)); - EXPECT_LT(slow_error, 10); + EXPECT_LT(slow_error, 50); } - for (auto e : errors) EXPECT_NEAR(e, errors[0], 1); + for (auto e : errors) + EXPECT_NEAR(e, errors[0], 1e-8); } } -TEST(Als, ComputeCorrectResult4D) -{ +TEST(Als, ComputeCorrectResult4D) { cals::Tensor X(5, {3, 3, 3, 3}); cals::Ktensor k(7, {3, 3, 3, 3}); k.randomize(); cals::AlsParams params; params.max_iterations = 100; + params.suppress_lut_warning = true; auto report = cp_als(X, k, params); // Get the slow error calculated by reconstructing the tensor, elementwise subtracting and norming - auto result_tensor = k.to_tensor(); - for (auto i = 0; i < X.get_n_elements(); i++) result_tensor[i] = X[i] - result_tensor[i]; - auto slow_error = result_tensor.norm(); + auto approx_X = k.to_tensor(); + for (dim_t i = 0; i < X.get_n_elements(); i++) + approx_X[i] = X[i] - approx_X[i]; + auto slow_error = approx_X.norm(); EXPECT_FALSE(std::isnan(slow_error)); EXPECT_LT(slow_error, 1e-1); } -TEST(Als, ComputeCorrectError) -{ +TEST(Als, ComputeCorrectError) { cals::Tensor X(5, {9, 3, 2}); cals::Ktensor k(5, {9, 3, 2}); k.randomize(); cals::AlsParams params; params.max_iterations = 3; + params.suppress_lut_warning = true; auto report = cp_als(X, k, params); // Get the fast error reported by the algorithm - auto fast_error = k.get_approx_error(); + auto fast_error = k.get_approximation_error(); // Get the slow error calculated by reconstructing the tensor, elementwise subtracting and norming - auto result_tensor = k.to_tensor(); - for (auto i = 0; i < X.get_n_elements(); i++) result_tensor[i] = X[i] - result_tensor[i]; - auto slow_error = result_tensor.norm(); + auto approx_X = k.to_tensor(); + for (dim_t i = 0; i < X.get_n_elements(); i++) + approx_X[i] = X[i] - approx_X[i]; + auto slow_error = approx_X.norm(); EXPECT_NEAR(fast_error, slow_error, 1e-10); } -int main(int argc, char **argv) -{ +int main(int argc, char **argv) { set_threads(4); ::testing::InitGoogleTest(&argc, argv); return RUN_ALL_TESTS(); diff --git a/tests/cals/CMakeLists.txt b/tests/cals/CMakeLists.txt index f013351..f2a54a0 100644 --- a/tests/cals/CMakeLists.txt +++ b/tests/cals/CMakeLists.txt @@ -1,42 +1,4 @@ -#@HEADER -# ****************************************************************************** -# -# CP-CALS: Software for computing the Canonical Polyadic Decomposition using -# the Concurrent Alternating Least Squares Algorithm. -# -# Copyright (c) 2020, Christos Psarras -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# 3. Neither the name of the copyright holder nor the names of its -# contributors may be used to endorse or promote products derived from -# this software without specific prior written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -# ****************************************************************************** -#@HEADER - set(tests test_cals) - foreach (test ${tests}) create_test(test.cals.${test} ${test}) endforeach () diff --git a/tests/cals/test_cals.cpp b/tests/cals/test_cals.cpp index 74c8e30..297e1ea 100644 --- a/tests/cals/test_cals.cpp +++ b/tests/cals/test_cals.cpp @@ -1,70 +1,32 @@ -//@HEADER -// ****************************************************************************** -// -// CP-CALS: Software for computing the Canonical Polyadic Decomposition using -// the Concurrent Alternating Least Squares Algorithm. -// -// Copyright (c) 2020, Christos Psarras -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// 1. Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// 2. Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// 3. Neither the name of the copyright holder nor the names of its -// contributors may be used to endorse or promote products derived from -// this software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR -// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT -// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY -// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// ****************************************************************************** -//@HEADER - #include "gtest/gtest.h" -#include #include +#include #include -#include "tensor.h" #include "als.h" #define MODEL_DIFF_ACC 1e-08 -class CalsParametersTests :public ::testing::TestWithParam {}; +class CalsParametersTests : public ::testing::TestWithParam {}; -TEST_P(CalsParametersTests, Correctness) -{ +TEST_P(CalsParametersTests, Correctness) { // parameter = GetParam() // You can pass a CalsParams object or a tuple of values and access them with std::get<1>(GetParam()) vector ranks; for (auto rank = 1; rank <= 10; rank++) - for (auto copies = 0; copies < 300; copies++) + for (auto copies = 0; copies < 30; copies++) ranks.push_back(rank); -// std::sort(ranks.begin(), ranks.end()); - std::shuffle(ranks.begin(), ranks.end(), std::default_random_engine(3454)); + // std::sort(ranks.begin(), ranks.end()); + std::default_random_engine generator; + std::shuffle(ranks.begin(), ranks.end(), generator); cals::CalsParams cals_params; - cals_params.mttkrp_method = cals::ops::MTTKRP_METHOD::AUTO; + cals_params.mttkrp_method = cals::mttkrp::MTTKRP_METHOD::AUTO; cals_params.max_iterations = 100; cals_params.tol = 1e-4; - cals_params.buffer_size = 300; + cals_params.buffer_size = 30; cals_params.line_search = true; cals_params.line_search_interval = 5; cals_params.line_search_step = 0.9; @@ -77,59 +39,47 @@ TEST_P(CalsParametersTests, Correctness) als_params.line_search_interval = cals_params.line_search_interval; als_params.line_search_step = cals_params.line_search_step; als_params.tol = cals_params.tol; -// als_params.cuda = cals_params.cuda; + als_params.cuda = cals_params.cuda; + als_params.suppress_lut_warning = true; cals::Tensor T(5, {9, 4, 3}); auto const modes = T.get_modes(); - vector ktensor_vector(ranks.size()); + int n_ktensors = static_cast(ranks.size()); + vector ktensor_vector(n_ktensors); auto i = 0; - for (auto &ktensor : ktensor_vector) - { + for (auto &ktensor : ktensor_vector) { ktensor = cals::Ktensor(ranks[i++], modes); ktensor.randomize(); } auto als_input(ktensor_vector); + auto als_omp_input(ktensor_vector); auto cals_input(ktensor_vector); - cals::KtensorQueue als_queue, cals_queue; - - for (auto p = 0lu; p < als_input.size(); p++) - { - als_queue.emplace(als_input[p]); + cals::KtensorQueue cals_queue; + for (auto p = 0; p < n_ktensors; p++) cals_queue.emplace(cals_input[p]); - } + cals::cp_cals(T, cals_queue, cals_params); - vector reports(als_queue.size()); + for (auto p = 0; p < n_ktensors; p++) + cals::cp_als(T, als_input[p], als_params); -// Sweep over Ktensor models. - i = 0; - while (!als_queue.empty()) - { - reports[i++] = cals::cp_als(T, als_queue.front(), als_params); - als_queue.pop(); - } + cals::cp_omp_als(T, als_omp_input, als_params); - cals::cp_cals(T, cals_queue, cals_params); - - auto index = 0; - for (auto &c: cals_input) - EXPECT_NEAR(als_input[index++].get_approx_error(), c.get_approx_error(), MODEL_DIFF_ACC); -// { -// if (als_input[index++].get_approx_error() - c.get_approx_error() > MODEL_DIFF_ACC) -// std::cerr << "Model missmatch" << std::endl; -// } + for (auto p = 0; p < n_ktensors; p++) { + EXPECT_NEAR(als_input[p].get_approximation_error(), cals_input[p].get_approximation_error(), MODEL_DIFF_ACC); + EXPECT_NEAR(als_omp_input[p].get_approximation_error(), cals_input[p].get_approximation_error(), MODEL_DIFF_ACC); + } }; -INSTANTIATE_TEST_CASE_P(CalsCorrectnessTests, CalsParametersTests, ::testing::Values(false)); +INSTANTIATE_TEST_SUITE_P(CalsCorrectnessTests, CalsParametersTests, ::testing::Values(false)); #if CUDA_ENABLED -INSTANTIATE_TEST_CASE_P(CalsCUDACorrectnessTests, CalsParametersTests, ::testing::Values(true)); +INSTANTIATE_TEST_SUITE_P(CalsCUDACorrectnessTests, CalsParametersTests, ::testing::Values(true)); #endif -int main(int argc, char **argv) -{ +int main(int argc, char **argv) { set_threads(4); ::testing::InitGoogleTest(&argc, argv); return RUN_ALL_TESTS();